AIFE 2025: Post-Conference Review
The recent AIFE 2025 conference took place at Nanyang Technological University, Singapore, from 18 to 20 Nov. This is the second run of the conference, which had its inaugural run from 4–5 Nov 2024. This year’s theme, “Theme: Learning About, With, and Beyond AI”, brought together educators, researchers, and technologists to discuss the evolving role of AI in education. This is the Singapore chapter; other similarly named conferences were held or are planned in other countries; Indonesia had one this year, with conferences planned next year in Japan, China, and Denmark.
Day 1 focused on “Learning About AI”, with talks and panels discussing AI literacy and curriculum development, as well as introducing current capabilities of AI tools being developed for education. Day 2 shifted to “Learning With AI”, showcasing practical applications of AI in classrooms, adaptive learning systems, and personalised education, and also panel discussions about the ethics of AI use and philosophical discussions on the purpose and future of AI in education. Finally, Day 3 explored “Learning Beyond AI”, looking at future trends, ethical considerations, and the broader societal impacts of AI in education.
My workplace sponsored me to attend this conference, and I took some notes on the sessions that I attended.
Note: I wrote this review with the help of AI, but it is not written by AI. Claude Sonnet 4.5 helped to look up and verify references, add citations, and generally ease the load of organising and editing my scribbled notes into a more coherent narrative. Any inaccuracies or misrepresentations are my own.
This review is written in three parts:
Part 1: Conference Summary
These are from my notes taken during the conference sessions that I attended.
Day 1: Learning About AI
Opening Keynote: Prof. Joseph Sung, Dean of Lee Kong Chian School of Medicine, NTU
Prof. Sung opened the conference with a medical metaphor that reframed the role of AI in education. He introduced the concept of “AI as maggot therapy” (Sung, 2024), drawing from maggot debridement therapy—a medical practice where maggots selectively consume dead tissue while preserving living tissue. In this analogy, AI handles routine and algorithmic tasks that are “no longer human”, allowing educators to focus on empathy, mentorship, and human connection.
Keynote: Prof. Simon See, Chief AI Technology Officer, NVIDIA AI Technology Centre
Prof. See provided a technical foundation for understanding modern AI systems. He explained emergent properties in large language models (LLMs)—capabilities that appear unpredictably as models scale. Importantly, Prof. See clarified that while the underlying mechanism (next-token prediction) remains constant, emergent capabilities appear in task performance at scale.
He addressed hallucinations in LLMs, explaining that these are inherent to how the systems work. Multiple mathematical proofs have demonstrated that LLMs cannot learn all computable functions (e.g., Xu et al., 2024; Banerjee et al., 2024). While techniques like Retrieval-Augmented Generation (RAG) and multi-agent verification can reduce hallucinations by 60–80%, complete elimination remains impossible.
Prof. See explained that AI systems are trained through reinforcement learning, the process by which AI models learn to align with human preferences and values.
He distinguished between traditional LLMs (reactive, responding to prompts) and agentic AI—systems that perceive, decide, and act autonomously, using tools and executing multi-step tasks proactively. Examples include ChatGPT with Code Interpreter and research agents like AutoGPT.
A critical insight for education emerged from Prof. See’s discussion of human–computer interaction: “We believe the pattern, not the mechanism.” Users anthropomorphise chatbots, applying a “person schema” to human-like text, following the heuristic “Sounds smart → Must be intelligent → Can be trusted.” This makes expert judgement MORE important in an AI-mediated world—experts reason from first principles and can identify when AI solves the wrong problem, even if it does so convincingly.
Panel Discussion 1: The Risks and Opportunities of AI for Teaching and Learning
Panelists: Prof. Simon See, Prof. Song Jie, Prof. Mutlu Cukurova, and Prof. Ho Shen Yong
The panel explored several interconnected themes around AI’s impact on education. They emphasised the development of judgement and taste—aesthetic, critical, and practical—as essential capabilities in an AI-augmented world. The discussion highlighted a shift toward task-aware learning, moving from problem-solving to problem-finding and problem-framing.
A tension emerged around humanisation: panelists expressed concern about dehumanised education, imagining lonely students with AI tutors replacing human interaction. However, others countered that AI could enable more human connection by handling routine tasks. On the question of bias, the panel understood this to be a tension to be managed rather than a problem to be solved.
The panel raised questions about efficacy versus deep learning: Does optimising for engagement metrics optimise for understanding? Can we distinguish between learning that looks effective and learning that is effective?
Parallel Session: Lambda Feedback – Prof. Peter Johnson, Imperial College London
Prof. Johnson presented Lambda Feedback, a microservice architecture for pedagogical judgements that addresses the problem of “pedagogy lock-in”. He explained that when educators adopt a platform, they also adopt all of the platform’s assumptions. It becomes difficult to unbundle specific features or frameworks, effectively stalling innovation when educators want to implement practices that don’t fit the platform’s embedded design.
The system employs modular services (math feedback, code feedback, writing feedback) that connect via standardised APIs. Lambda Feedback decouples feedback generation from the Learning Management System, allowing educators to plug in specialised feedback services as needed.
Prof. Johnson posed the question: What would a standardised API schema for educational feedback look like? He invited collaborators interested in developing interoperability standards.
Parallel Session: Personal Feedback at Scale—Dr. Mohamed Arif Mohamed
Dr. Mohamed presented an OCR–LLM system for generating personalised feedback at scale. The workflow involves: scanning student work, matching against an instructor comment bank, generating drafts in the instructor’s voice, and implementing human-in-the-loop review before delivery to students.
Drawing on feedback literature, Dr. Mohamed emphasised principles for effective feedback that reflect ideas similar to contemporary work on reconceptualising feedback practice (Boud & Molloy, 2013): feedback should be contextual (referencing class content), relational (operating in a shared teacher–learner space), and personal (conveying authentic voice). The system addresses a specific problem: the emergence of copy–paste ChatGPT feedback that students recognise as inauthentic and generic. By maintaining the instructor’s voice and requiring human review, the system aims to preserve the relational dimension of feedback while scaling the instructor’s capacity.
Parallel Session: Process Telemetry in Learning – Dr. Ho Jia Xuan
Dr. Ho introduced process telemetry that records keystrokes, mouse movements, pauses, and edits with timestamps. This approach has foundations in 1990s writing process research (Levy & Ransdell, 1994) and can infer cognitive states: long pauses suggest planning or confusion, burst–pause–burst patterns indicate fluent writing, repeated revisions signal struggle, and high deletion rates may indicate anxiety.
Dr. Ho’s focus was on using telemetry to observe process rather than just product. For example, detecting repeated edits that do not improve the writing—such as a student who kept editing the first sentence of an abstract without noticeably improving its clarity. He noted that large chunks of copy–paste could indicate AI use. This represents an evolution from outcome analytics (grades, completion rates) to process analytics (how learning happens).
Day 2: Learning With AI
Keynote: Prof. Miao Chunyan, Vice-President (Innovation & Entrepreneurship), NTU
Prof. Miao introduced a framework of four types of agentic AI in learning, each grounded in learning theory:
- Teachable AI—Based on Learning-by-Teaching theory (Martin, 1985), where students teach an AI agent that acts as a novice learner
- Curious AI—Drawing on Berlyne’s theory of curiosity (1960), incorporating optimal complexity, novelty seeking, information gaps, and the drive to reduce uncertainty
- Persuasive AI—Built on the Elaboration Likelihood Model (Petty & Cacioppo, 1986), distinguishing between central routes (logical argument) and peripheral routes (emotional appeal)
- Remembrance AI—Implementing the spacing effect (Ebbinghaus, 1885), spaced repetition algorithms, and techniques for building remote associations
Prof. Miao presented the CLC Virtual Singapura case study, a 3D virtual learning environment using affective teachable agents. Students teach AI agents (such as water molecules that need to learn about diffusion) by constructing concept maps. The research showed that affective teachable agents can improve student reflection and learning outcomes.
Keynote: Prof. Mutlu Cukurova, UCL Knowledge Lab, University College London
Prof. Cukurova presented a framework describing different ways AI can be conceptualised in educational contexts, which he applied to teacher-AI interaction (Cukurova, 2024):
Mode 1: Replacement—AI takes over entire tasks. Concerns include accuracy issues and deskilling of educators.
Mode 2: Amplification—AI enhances human capability while humans retain control. Prof. Cukurova emphasised that amplification requires internalisation—understanding how the AI works, not just its outputs. This is analogous to understanding a car’s mechanics, an electric screwdriver’s torque settings, or a cooking appliance’s heat distribution. Without internalisation, users cannot reason from first principles about AI behavior, troubleshoot problems, or judge output quality. With internalisation, users achieve true complementarity between human and AI capabilities.
Mode 3: Augmentation—Human-AI synergy creates emergent capabilities. This mode is aspirational and rarely achieved in current systems.
Prof. Cukurova noted that most systems aim for Mode 1 (replacement), but education should aim for Mode 2 (amplification with internalisation) with eventual progression to Mode 3.
He introduced the concept of cognitive atrophy—the loss of cognitive skills from AI over-reliance. Mechanisms include skill decay (“use it or lose it”), learned helplessness, and reduced productive struggle. Examples include the calculator effect on mental math and GPS effects on spatial navigation. Prof. Cukurova posed a counter-question: Perhaps we should let some skills atrophy to focus cognitive resources on higher-order capabilities?
Prof. Cukurova discussed praxical teaming, where AI learns from teacher corrections over time, building a more accurate model of teacher preferences and pedagogical judgment. He noted that this requires existing task synergy and raised appropriate skepticism—corrections may not transfer to novel contexts.
Regarding GenAI for lesson planning, Prof. Cukurova cited specific data showing 31% time reduction (from 81.5 minutes to 56.2 minutes). He briefly mentioned that a shortcoming of these findings is that they don’t address whether lesson quality improves—planning faster doesn’t necessarily mean planning better.
Panel Discussion: Emotions in Learning
Chair: Asst. Prof. Tanmay Sinha (NTU/NIE)
Panelists: Dr. Yang Yang (NTU/NIE), Dr. Aishah Abdul Rahman (NTU/NIE), Dr. Lee Vwen Yen Alwyn (NTU/NIE)
The panel began with a profound assertion: “All learning is emotional.” Panelists drew from multiple research streams:
Early literacy and socio-emotional development (ages 4–7), exploring how emotional states affect foundational skill acquisition.
Emotion socialization through conversation. Research on parental emotion socialization explores how different conversational approaches affect children’s emotional development, including distinctions between discussing internal emotional states versus focusing on behavior (Eisenberg et al., 1998).
Executive function—The cognitive control processes of working memory, inhibition, and cognitive shifting (Diamond, 2013). Emotional dysregulation impairs executive function, which in turn impairs learning.
Yerkes-Dodson Law—The inverted U-curve relationship between arousal and performance (Yerkes & Dodson, 1908). Optimal performance occurs at moderate arousal levels. Too little arousal leads to boredom; too much leads to anxiety. This suggests that some frustration is actually beneficial for learning.
Epistemic emotions—Emotions specifically related to knowledge-building: curiosity, confusion, surprise, and “aha moments” (Pekrun & Stephens, 2012). These emotions signal the learning process itself.
The Emosense project achieved 65% accuracy for detecting enjoyment, but only 33% for boredom and confusion. Research showed negative emotions at task start, with positive emotions at task end if students succeeded—suggesting that emotional trajectories matter more than emotional states at any single moment.
Panelists discussed productive struggle through the lens of Self-Determination Theory (Deci & Ryan, 2000), which identifies three psychological needs: autonomy, competence, and relatedness. Teacher-learner trust enables risk-taking necessary for productive struggle.
They explored uncertainty tolerance across three dimensions: behavioral (acting wisely despite uncertainty, craving novelty), cognitive (accepting that uncertainty is inherent to learning), and affective (managing the discomfort of not knowing).
A critical question emerged: If AI removes all uncertainty from learning tasks, does learning still happen? Can AI scaffold productive struggle without eliminating the struggle that produces learning?
Parallel Session: Performative Assessment—Dr. Ricky Chua
Dr. Chua presented a solution to AI-facilitated cheating in presentations using the SAMR Model—a widely-used framework for technology integration developed by Ruben Puentedura, though not formally published in peer-reviewed literature:
- Substitution: Technology replaces a tool with no functional change
- Augmentation: Technology replaces a tool with functional improvement
- Modification: Technology enables significant task redesign
- Redefinition: Technology enables previously inconceivable tasks
His performative assessment solution imposed strict constraints: maximum 5 words per slide, script allowed only in notes (not visible to audience), no script reading during presentation, and images as memory triggers rather than content carriers. This design decouples what AI can do (generate slides and scripts) from what is assessed (live presentation skill and conceptual understanding).
Dr. Chua reported unintended outcomes: a 3% drop in teaching evaluations (students found the format more demanding), but students reported feeling the assessment was easier to prepare for, allowed more creativity, and gave them more control over their performance. The format made it immediately obvious when students didn’t understand their material—they couldn’t hide behind well-crafted slides.
Parallel Session: AI in Art and Design Education—Assoc. Prof. Lisa Winstanley
Assoc. Prof. Winstanley articulated concerns specific to creative disciplines: environmental cost of AI image generation, copyright violation in training data, algorithmic bias in outputs, homogenization of aesthetic (“AI slop”), and most critically—AI undermines the creative process itself.
She argued that in art and design education, the struggle IS the learning. The iterative process of ideation, failure, revision, and discovery cannot be separated from the outcome. When AI generates images instantly, students bypass the very process they’re meant to master.
Assoc. Prof. Winstanley’s recommendations included: teach transparency about AI’s limitations and training data, assess process documentation rather than finished products, allow students to ethically refuse AI use when it conflicts with learning goals, and encourage slow design—valuing iteration and failure as essential to creative development.
During panel discussion, insights emerged: students often use AI to avoid engagement in mandatory modules outside their major; could AI serve as a provocateur to spark creative thinking rather than resolve it?; and the importance of scaffolding—students should master basics first, then use AI for higher-order creative exploration once foundational skills are secure.
Day 3: Learning Beyond AI
Opening Remarks: Prof. Liu Woon Chia, Director of NIE, NTU
Prof. Liu Woon Chia opened Day 3 by introducing the day’s focus on innovative AI solutions and their proper and responsible use in education. She emphasized the importance of ethical AI policies as NIE advances its research agenda in AI for education.
Keynote: Prof. Mairéad Pratschke, MIT STEP Lab
Prof. Pratschke presented findings from MIT’s research titled “Your Brain on ChatGPT”. She emphasized a fundamental principle: learning activities should match learning outcomes—a concept known as Constructive Alignment (Biggs, 1996).
The problem: If the intended learning outcome is “analyze arguments critically,” but the learning activity is “get ChatGPT to analyze arguments,” there’s a profound misalignment. The student doesn’t practice the skill they’re supposed to develop. Prof. Pratschke critiqued industry responses to educational concerns, noting that companies add surface features (like “study mode” or “education plans”) without understanding how learning actually works. Industry examples include ChatGPT’s “guided learning mode” and Gemini’s educational features—but these are “tweaks to model behavior rather than pedagogically sound learning design.” These changes confuse interface modifications with pedagogical design.
She discussed AI as presence, noting that different forms create different relationship expectations. The extended list includes: chatbots, avatars, assistants, tutors, agents, humanoid robots, and digital humans. Prof. Pratschke charted AI development along two dimensions: intelligence (capabilities) × integration (embedding). The design implication: form should match function and align with learning theory.
Prof. Pratschke introduced “reasoners” or “thinking machines”—a new class of AI systems (2024–2025) that show reasoning steps explicitly, such as OpenAI’s o1 and Anthropic’s extended thinking models. These systems make reasoning visible and auditable, potentially modeling problem-solving processes for students. However, she cautioned that they may encourage mimicking the visible reasoning pattern over developing genuine understanding.
She presented the memory paradox: Declarative knowledge (knowing THAT—facts and information) versus procedural knowledge (knowing HOW—skills and procedures). She emphasized that “cognitive function depends on storing key information—this is a pillar of learning.” Skills equal procedural knowledge plus context. AI can store infinite declarative knowledge, but procedural knowledge requires practice. If AI executes procedures, students don’t develop procedural knowledge. Drawing on Schema Theory (Bartlett, 1932; Sweller, 1988), Prof. Pratschke explained that learning requires building mental schemas, schemas require storing key information in long-term memory, if AI stores information externally then schemas don’t form, and without schemas one cannot develop expertise.
She highlighted a shift from prompt engineering to context engineering. In 2025, effective AI use requires managing the entire context window: system prompts, retrieved documents (RAG), conversation history, tool definitions, few-shot examples, and user data. This is more complex than crafting the right prompt—it requires understanding what context is needed, managing token limits, and ensuring quality and coherence across all context components.
Prof. Pratschke introduced a Pedagogical Systems framework: Users interact via prompting, retrieval, and tool calling → Context Window → LLM. She then outlined four intelligences needed for effective AI in education (note: this differs from her earlier three-type framework):
- Cognitive intelligence—Understanding AI capabilities and limitations
- Learning intelligence—Understanding how learning happens
- Pedagogical intelligence—Understanding teaching practices
- Technical intelligence—Understanding implementation details
She also discussed learning from experience in virtual worlds and introduced the Agentic Community of Enquiry framework, which includes cognitive agents, pedagogical agents, and social agents.
Prof. Pratschke introduced a Contextual Intelligence Framework identifying three types of intelligence needed for effective AI in education:
- Computational intelligence—Understanding AI capabilities and limitations
- Pedagogical intelligence—Understanding how learning works
- Content intelligence—Understanding subject matter deeply
All three are required. Most current solutions are strong on computational intelligence but weak on pedagogical and content intelligence.
She referenced Karpathy’s Software 3.0 framework: Software 1.0 uses explicit programming (if-then rules), Software 2.0 uses machine learning (examples → learned patterns), and Software 3.0 uses foundation models plus context (LLMs configured through prompts and context). This represents a shift from code to data to prompts/context as the primary interface for creating software behavior.
Keynote: Prof. Phillip Dawson, Deakin University
Prof. Dawson opened with a core principle: “If AI can do your assessment task and pass, you have an assessment design problem.” This reframes the challenge from detection to design.
He noted that students are currently using AI to:
- Explain concepts
- Summarize materials
- Suggest research ideas
Unsupervised multiple choice is no longer reliable for summative assessment. ChatGPT Agent can already complete an online course mostly smoothly.
Guiding principles for assessment reform:
- Assessment & learning experiences equip students to participate ethically in an AI-ubiquitous society
- Forming trustworthy judgments about student learning requires multiple, inclusive, contextualized approaches
Referenced: TEQSA - “Assessment reform for the age of AI”
Prof. Dawson presented on assessment design for the AI era, reframing the challenge from “How do we detect AI?” to “How do we design assessments that AI can’t easily complete?” This shifts the focus from policing students to designing better assessments.
He presented the TEQSA framework (Lodge et al., 2023)—Australia’s Tertiary Education Quality and Standards Agency guidelines—with five propositions:
- Emphasize appropriate, authentic AI engagement—Rather than banning AI, teach students when and how to use it appropriately
- Systemic program assessment—Design assessment at the program level, not just individual assignment level
- Emphasize process—Assess learning journeys, not just products
- Provide opportunities for collaboration—Both human-human and human-AI collaboration
- Strategic security at meaningful points—Apply resource-intensive security only at key progression/completion milestones
Prof. Dawson emphasized: What we assess matters more than how we assess. Assessment validity matters more than cheating prevention—don’t hurt validity to block AI.
Prof. Dawson emphasized that validity should take precedence over cheating prevention. He introduced future-authentic assessment—if AI will be ubiquitous in future work contexts, then assessing students without AI doesn’t prepare them for reality. We should assess AI-augmented performance, focusing on judgment, creativity, and synthesis skills.
He presented the concept of reverse scaffolding: Traditional scaffolding provides more support early and gradually reduces it; reverse scaffolding prohibits AI use early (when students are developing foundational skills) and allows AI use once skills are mastered. For example: Year 1 students write without AI, Year 2 students can use AI for drafting but must show revision processes, Year 3 students have full AI access but are assessed on judgment and quality of final work. This prevents skill atrophy while allowing efficiency gains.
Prof. Dawson revisited Zone of Proximal Development (ZPD) (Vygotsky, 1978)—tasks that students cannot complete alone but can complete with appropriate support. This is the “sweet spot” for learning. The problem with many AI tools: traditional scaffolding adjusts to learner level, but AI tools often jump directly to expert-level performance, bypassing the ZPD entirely. No learning happens when students skip from novice directly to expert output.
The solution: design AI tools that provide graduated support, maintaining students within their ZPD.
He explained Cognitive Load Theory (Sweller et al., 1998) with three types of cognitive load:
- Intrinsic load—Inherent complexity of the material (KEEP—this is learning)
- Extraneous load—Poor design and distractions (REMOVE—this wastes cognitive capacity)
- Germane load—Processing that builds schemas (SUPPORT—this is learning)
AI should remove extraneous load and support germane load, but NOT remove intrinsic load. Example: AI formatting citations is appropriate (removes extraneous load); AI writing arguments is inappropriate (removes intrinsic load that constitutes the learning itself).
Prof. Dawson introduced evaluative judgment (Tai et al., 2018)—the capability to judge quality of work, both one’s own and others’. This is essential for self-regulated learning and develops through practice. In AI contexts, students must judge AI outputs, decide what to keep or revise, and evaluate appropriateness of AI use for specific tasks. However, evaluative judgment alone is not sufficient—students need additional capabilities to work effectively with AI.
He distinguished between discursive and structural assessment approaches: Discursive: Instructions about AI use (“You may use AI for editing but not drafting”; “Cite all AI use”)
- Structural: Task requirements that make certain behaviors unavoidable (oral defenses, live coding, iterative submissions showing process, portfolios with reflections)
Prof. Dawson expressed preference for structural approaches as they’re harder to circumvent than discursive approaches, which rely on student compliance.
Panel Discussion 3: Assessment and AI in Singapore’s Education System
Chair: Assoc. Prof. Teo Tang Wee (NTU/NIE)
Panelists: Prof. Mairéad Pratschke, Prof. Phillip Dawson (joining virtually), Mdm Lee Lin Yee (Divisional Director, Educational Technology Division, MOE), Mr. Jason See (Chief Information Officer / Divisional Director, Digital Excellence & Products Division, MOE)
The panel explored critical questions about the future of assessment in an AI-enabled education system:
Question: What does the new hybrid model mean for teachers and students? The panelists discussed how hybrid human-AI models transform both teaching and assessment practices.
Question: What are international best practices for AI in assessment?
Discussion covered:
- Students already using AI for feedback
- Should AI be used for summative work?
- What should educators be able to do with AI?
Question: AI can do a lot—but should it?
Mdm Lee emphasized: Encourage schools to experiment, fail fast, fail forward. She highlighted the importance of unifying learning and assessment rather than treating assessment as separate and summative.
A critical question emerged: Planning lessons faster—but is the lesson better?
The panel discussed tools for teachers including:
- How to pick up signals about student progress
- Wellness tools for managing screen time
Question: Policy and technology as enablers
Mr. Jason See framed policy and technology as “seatbelts”—they help people feel safe (particularly regarding data privacy) so they can move fast.
Key insights:
- Context and infrastructure determine what we can and cannot do
- As users, we are responsible for educating the tools and platforms we use
- Tools work if used well; need to equip students and teachers with knowledge on how to use and how to learn (meta-learning)
Parallel Session: Empowering Students in the AI Age—Prof. Venky Shankararaman, Vice Provost (Education), Singapore Management University
Prof. Shankararaman presented SMU’s approach to integrating AI across the curriculum.
SMU’s DRIVE Approach
SMU follows a Detect-Incorporate-Adapt framework, with detection as a last resort. The DRIVE approach is intentionally designed to be difficult, encouraging faculty to adapt and incorporate AI rather than rely on detection.
Key Challenges:
- Overreliance on AI
- Curriculum relevance
- Assessment validity
- Equity and access
- Ethical and societal literacy
- Faculty readiness
Curriculum for the AI Age
Essential competencies:
- Critical thinking and judgment
- Human-human and human-AI collaboration
- Data literacy and digital fluency
- Creativity and problem-solving
- Ethics, policy, and responsible use
Guiding Principles:
- Universality with depth
- Ethics first
- Applied and experimental
- Interdisciplinary
- Agility
- Access and inclusion
Ideal Curriculum Structure:
- Year 1: Foundation—building critical thinking skills
- Year 2: Applied Skills—hands-on practice
- Year 3: Strategic Integration—discipline-specific AI applications
- Year 4: Capstone & Real Impact—authentic projects
Panel discussion raised a critical question: “How do we teach critical thinking, and what tangible outcomes of critical thinking can we assess?”
Suggestions included:
- Data analysis questions with additional data to observe responses
- Recognition that critical thinking is discipline-specific and depends on domain details
Parallel Session: Systemic Framework for Assessment & Curriculum Design—Dr. Ho Shen Yong, Executive Director of InsPIRE, NTU
(Note: Presented on behalf of original presenter)
Dr. Ho introduced a two-lane approach to assessment:
Lane 1: Assessment of learning (summative, measuring outcomes)
Lane 2: Assessment for and as learning (formative, supporting learning process)
He referenced the SOLO Taxonomy (Structure of Observed Learning Outcomes) for evaluating learning depth and the 85% rule for optimal learning—students should succeed approximately 85% of the time to maintain motivation while being appropriately challenged.
Assessment 2×2 Framework:
| No AI Allowed | AI Assisted | |
|---|---|---|
| Supervised | Traditional exams | Oral defense, live coding |
| Unsupervised | Take-home assignments | Unreliable—expect AI use |
Key insight: Unsupervised, AI-assisted assessments are unreliable and should be avoided. We should expect students to use AI in these contexts.
The panel discussion explored how to assess critical thinking:
- Provide additional data and observe student responses
- Recognize that critical thinking is discipline-specific
Parallel Session: Digital Portfolio Implementation and AI Reflection Assistant — NIE Team
The NIE team presented their work on digital portfolios aligned with Singapore’s TE²¹ Model (Teacher Education for 21st Century)—NIE’s framework guiding teacher education since approximately 2009.
The TE²¹ Model identifies three core competencies:
- Professional Practice
- Personal Growth and Development
- Leadership and Agency
And five teacher roles:
- Shapers of Character
- Creators of Knowledge
- Facilitators of Learning
- Architects of Learning Environments
- Agents of Educational Change
The team distinguished three types of digital portfolios:
Learning Portfolio—Process and developmental focus, documents the learning journey, audience is self and teacher, content includes drafts/reflections/growth evidence, used for formative assessment
Showcase Portfolio—Presentation focus, displays accomplishments and best work, audience is external (employers, admissions), content is polished products, used for summative assessment
Teaching Portfolio—Professional focus, documents teaching practice and philosophy, audience is supervisors/tenure committees, content includes lesson plans/student work/teaching reflections, used for professional evaluation
The NIE implementation focuses on learning portfolios to support pre-service teacher development. The reseach team also presented their AI Reflection Assistant, designed with principles aligned with Self-Determination Theory (Deci & Ryan, 2000):
Autonomy support—The AI encourages thinking rather than providing answers, prompting students to develop their own insights
Competence support—The AI develops students’ reflective capability rather than doing the reflection work for them
The design is growth-focused: it avoids giving answers (which creates dependency) and instead asks questions that scaffold deeper reflection.
The team adapted Gibbs’ Reflective Cycle into a four-level framework:
- What happened? (Description)
- Why? (Analysis & Interpretation)
- So what? (Meaning & Application)
- Now what? (Implications for Action)
This condenses Gibbs’ original six stages. Alternative models referenced include Rolfe et al. (2001) and Borton (1970) with their “What? So what? Now what?” structure.
The team described a reflection depth hierarchy informed by Hatton & Smith’s (1995) research on reflective writing, focusing on three levels of reflective practice:
- Descriptive (surface)—Simply recounting what happened
- Dialogic (exploring)—Questioning and exploring multiple perspectives
- Critical (deep)—Examining underlying assumptions and power structures
The AI’s role is to move students progressively through these levels.
Preliminary findings indicated that students find the AI prompting helpful without feeling intrusive, they value directional guidance over prescriptive answers, and the approach aligns with growth mindset development.
A critical design principle emerged: human agency. The learner must take an active role in interpreting and engaging with feedback. The AI provides prompts, directions, and frameworks, but the HUMAN does the thinking, makes connections, draws conclusions, and plans actions. This prevents copy-paste compliance, surface engagement, dependency, and loss of reflective capacity. The design principle: AI as catalyst, not provider.
Parallel Session: Knowledge Building with AI – Dr. Lydia Cao (University of Toronto), Assoc. Prof. Bodong Chen (University of Pennsylvania), Dr. Teo Chew Lee (NTU/NIE), and Dr. Katherine Yuan (NTU/NIE)
This collaborative session explored integrating AI into Knowledge Building (KB) pedagogies. Dr. Chen opened by contrasting Knowledge Building with traditional knowledge transmission:
Transmission model: Teacher possesses knowledge → transmits to students → students receive and memorize → assessment measures acquisition
Knowledge Building (Bereiter & Scardamalia, 1993): Collaborative advancement of community knowledge, students as knowledge creators not just consumers, focus on idea improvement not just learning, collective responsibility for community’s knowledge growth, modeled on how scientific communities actually work.
Historical context includes social constructivism (Vygotsky), communities of practice (Wenger), and Computer-Supported Collaborative Learning (CSCL). The technology platform is Knowledge Forum (originally CSILE).
The session covered the 12 Knowledge Building Principles (Scardamalia, 2002), highlighting several in detail:
Principle 5: Epistemic Agency—Students as agents of their own knowledge advancement. They set goals, decide which problems to tackle, control the inquiry process, and take responsibility for community knowledge growth.
Principle 11: Knowledge-Creating Dialogue—Building on ideas, identifying contradictions, seeking evidence, reformulating theories, aiming for continuous improvement. Dialogue is tentative and exploratory; questions are valued; “I don’t know” is a legitimate and productive position.
Principle 12: Transformative Assessment—Assess the state of community knowledge, identify the cutting edge of understanding, feed forward to next problems. Assessment transforms into new inquiry rather than terminating learning.
A critical design question emerged: How do we distribute epistemic agency between AI and students? Traditional KB gives students full epistemic agency. With AI integration, how much agency goes to AI versus students? This is a delicate balance—if AI suggests problems, it reduces student agency; if AI generates ideas, it reduces student ownership; if AI decides direction, it undermines KB principles.
Dr. Lydia Cao introduced the Idea Constellation Map—a visualization tool bridging the inward view (what OUR community understands) and the outward view (what the WORLD knows—frontier knowledge). The gap between these views is where the community can grow.
Features include: zooming in to see detail, uncovering hidden connections (AI finds links students might miss), and overlaying community knowledge against world knowledge. Example: A community studying climate change maps their collective understanding, then AI overlays this against the scientific frontier, revealing gaps and opportunities for knowledge advancement.
This represents sophisticated AI use—not giving answers or generating content, but providing perspective on community knowledge and scaffolding metacognition about collective understanding.
Assessing collaborative knowledge building emerged as an open challenge during the session. How do we assess community knowledge (not individual achievement), idea improvement (not mastery of fixed content), and collective advancement (not grades and ranking)? Traditional metrics fail here: individual tests measure personal knowledge, grades assume stable endpoints, ranking assumes competition. Potential approaches include semantic network analysis, KB discourse analysis, idea trajectory analysis, and “rise above” analysis. AI could help track idea evolution, map knowledge networks, identify emergent patterns, and visualize collective progress—but human judgment is still needed to determine what constitutes genuine “advancement.”
The KB Loop provides a recursive cycle:
- Questions/Problems →
- Create ideas & artifacts →
- Experiment/test →
- Share/build/connect → (back to 1)
All stages lead to better ideas. The cycle is continuous with no fixed endpoint—knowledge building is about ongoing improvement. AI’s potential role at each stage: help identify gaps and generate questions, scaffold idea generation, support experimental design, and facilitate connection-making. But students must drive the cycle.
Dr. Chen also presented some work by students on integrating ChatGPT into their KB activities, identifying two promising interaction patterns that maintain student epistemic agency:
Pattern 1—Explore Problem Space: Students face a complex problem, ChatGPT generates multiple possible approaches, students evaluate and select among options. AI expands the possibility space; students maintain decision-making authority. Example: Problem of reducing school waste → ChatGPT suggests 10 different approaches → students evaluate feasibility and choose direction. Key principle: AI generates options, students exercise judgment.
Pattern 2—Build Through Discourse: Students propose an explanation, ChatGPT plays devil’s advocate by asking probing questions and identifying weaknesses, students must defend or revise their thinking. Example: Student claims “Recycling solves the waste problem” → ChatGPT responds “What about contamination rates? What are the energy costs of recycling?” → student must address these challenges. Key principle: AI critiques, students improve ideas.
This is pedagogically sophisticated—AI doesn’t replace student thinking but scaffolds the discourse process while students do the knowledge building work. The proposed solution ensures AI provides options while students maintain choice and decision-making authority.
CraftPad was introduced as a design tool for teachers to plan KB activities, map student knowledge, and design interventions. Features include: flexible workspace with infinite canvas, context-aware KB coach (understands current KB community state, suggests scaffolds based on where the community is, adapts to teacher’s goals), and rich opportunities for teacher decisions (AI provides options, teacher makes pedagogical choices, maintains teacher agency).
CraftPad exemplifies AI augmenting professional judgment rather than replacing it.
Closing Keynote: Hung & Tan—Learning vs. Performance Augmentation
The closing keynote drew a critical distinction between learning augmentation and performance augmentation:
Performance Augmentation: Goal: Improved output (better products, faster completion)
- User role: Operator of AI tool—Example: Grammarly polishing writing—Outcome: Better product—Learning: Minimal or none
Learning Augmentation: Goal: Improved understanding and capability—User role: Active learner with AI support—Example: Using AI to explore concepts and receive feedback on thinking—Outcome: Better learner—Learning: Central purpose
The danger identified: Current AI use in education focuses heavily on performance augmentation, which doesn’t build capability and may actually reduce learning through cognitive offload.
Examples contrasting the two approaches:
Writing an essay: Performance: AI writes, student submits—Learning: Student writes, AI questions the reasoning
Solving math problems: Performance: AI solves the problem—Learning: AI shows steps, student explains the reasoning
Coding: Performance: AI generates complete code—Learning: AI explains concepts, student writes code
Research: Performance: AI summarizes sources—Learning: AI helps student synthesize across sources
The key principle: In performance augmentation, AI does the work; in learning augmentation, the student does the work while AI supports thinking.
This framework echoed themes throughout the conference: Prof. Cukurova’s distinction between replacement and amplification, Prof. Dawson’s emphasis on assessing process not just product, and Knowledge Building’s focus on epistemic agency.
The closing keynote tied together the conference’s three-day progression:
- Day 1: Learning ABOUT AI—Understanding how AI works
- Day 2: Learning WITH AI—Using AI as a tool
- Day 3: Learning BEYOND AI—Using AI to augment learning without bypassing it
“Learning Beyond AI” was interpreted as using AI to augment learning rather than replace it—developing human capabilities that transcend what AI can do, preparing for more advanced AI while maintaining human agency, and building collective intelligence that goes beyond individual AI assistance.
[End of Part 1]
Part 2: Personal Thoughts and Responses
These are my thoughts and responses to each individual talk/discussion, as I recall them.
Day 1: Learning About AI
Opening Keynote: Prof. Joseph Sung, Dean of Lee Kong Chian School of Medicine, NTU
I like the analogy of “maggot therapy” in education. There is little to disagree with here for me, only more questions about which parts of education would be considered “necrotic tissue” that AI should help remove. What would the consensus among the teaching fraternity look like?
Keynote: Prof. Simon See, Chief AI Technology Officer, NVIDIA AI Technology Centre
Prof. See’s keynote was a good overview of the state of AI technology, although I have been through enough AI introductory materials to find little that was new. The need to be aware of hallucinations and biases in AI outputs is well known, but the challenge remains: how do we teach students to be aware of these issues, and to critically evaluate AI outputs? Also, how might we design lessons where these hallucinations and biases are a feature, not a bug?
Panel Discussion 1: The Risks and Opportunities of AI for Teaching and Learning
Good points raised about dehumanised education, and being careful not to confuse learning efficacy with learning depth. So far there are more questions raised than clear answers, as the opening keynote promised.
Parallel Session: Lambda Feedback – Prof. Peter Johnson, Imperial College London
I struggled with the same thing that Prof. Johnson raised: the lack of schema for AI assessment makes it difficult to collaborate, because there is no common format or termonilogy for representing the taxonomy of assessment. I hope in future there’ll be a chance to contribute to this schema development, especially for local contexts.
Parallel Session: Personal Feedback at Scale—Dr. Mohamed Arif Mohamed
I’ve tried using AI to do assessment at scale, and found it really challenging; I ended up spending more time on that than I would have spent doing the grading manually. The challenges are as he highlighted: the difficulty of passing to the AI relevant context about the student, the assignment, and the grading rubric.
I’m glad Dr Arif found a process that worked for him, though if I try to engineer this process for myself I would try to avoid OCR as much as possible and try to keep artifacts digital as much as possible.
Parallel Session: Process Telemetry in Learning – Dr. Ho Jia Xuan
This was extremely fascinating! I notice parallels with students who struggle in the process of programming.
Many students attempt to write code linearly, that is, from top to bottom, left to right, one line at a time. But in my experience, experienced programmers tend to write code inside-out: they figure out the smallest step that needs to be carried out, then wrap it in a loop if it needs to be repeated, in a function if it needs to be reused, and so on. The process is more iterative and non-linear.
Having a tool to measure and visualise process telemetry could help me more easily identify students who are stuggling with this. In the classroom I can only see a snapshot of the student’s code at any point in time, and seldom the process they took to get there.
Day 2: Learning With AI (AI in Context – Applications Across Fields)
Opening Keynote 3: Prof. Miao Chunyan – Four Types of Agentic AI in Learning
Teachable AI is such an interesting idea! It’s difficult for a teacher to roleplay a learner, mainly because children and teenagers are not easily fooled and they know when you are withholding an answer. So we mainly role-model curiosity, which is still different from teaching; the student is still relying on you (as teacher) to make intelligent guesses about what they’re trying to convey, rather than being motivated to express ideas clearly (by trying to create coherence in their own model).
Hence, teachable AI: an AI that takes the role of student. AI agents are pretty good at role modelling. But the base models that most AI agents use have been trained on a large dataset that would likely include information that they are “not supposed to know”. The agents can roleplay ignorance, but cannot escape the possibility of prompt injection or accidental info leak in conversation. Would it be possible to train models with just the baseline information and world model of a “true” learner? Even a whole collection/community of models? That would make for a really interesting large-scale experiment.
Keynote 4: Prof. Mutlu Cukurova – Teacher-AI Teaming and Cognitive Atrophy
Very valid concerns raised by Prof. Mutlu. I’ve heard similar concerns about cognitive decline (from AI replacement) from the software engineering industry which is one of the first to adopt AI. Other industries will unfortunately likely have to learn their own lessons from experience.
Designing for augmentation, however, is challenging: both the user and the AI will have to share a common (work)space where the work is done. I don’t see much research being done in this area; the little I’ve read raises Joint Cognitive Systems as a model for this. My connection, Cedric Chin of CommonCog, recently hosted a podcast with Lia Dibello and Neil Sahota on Human-AI Symbiosis, where they discuss what it looks like for both AI and humans to learn together (about the work to be done).
I know intuitively what augmentation does not look like though: prompt engineering. Alas, this is where the bulk of the leading edge for non-technical folks is at, and it is what many AI training courses are trying to push me towards. Prompt engineering is what you do when AI does not have the context you have. This is at best amplification, at worst replacement: you copy-paste content into the AI, give it carefully worded instructions, it does the work.
More on this in my thoughts on Prof. Pratschke’s keynote.
Panel Discussion 2: Asst. Prof. Tanmay Sinha (Chair), Dr. Yang Yang, Dr. Aishah Abdul Rahman, Dr. Lee Vwen Yen Alwyn – Emotions in Learning
This is probably the first session with the most takeaway for me. While we did cover this briefly in my post-graduate diploma programme, the session provided a richer taxonomy of aspects of socio-emotional learning and the emotional aspect of learning: what does learning feel like for learners?
For a teacher the effect of emotions that students bring into learning is undeniable, and the use of AI in education cannot be naive to this. This naivety is one of the huge pitfalls of any educational chatbot at present. The application of emotions in learning goes beyond the chatbot’s prompting, I think it has to be present even when designing the UX of learning—the learning experience. This was already true even without using AI.
Parallel Session T2d: Dr. Ricky Chua – Performative Assessment and the SAMR Model
I’ve always believed that presentations should be carried out without a physical script, so Dr Chua’s idea resonated a lot with me. And it makes sense that this is hard to fake with AI.
In evolutionary biology, the concept of honest signals is quite different from everyday usage of the word “honest”. Whereas we use “honest” to communicate intent i.e. lacking intention to deceive, an honest signal is a signal that reliably communicates information. A healthy peacock tail reliably conveys that the peacock is fit enough to escape predators and healthy enough to produce beautiful plumage; A stotting gazelle demonstrates an excess of fitness and ability.
The question then, is: how do we define and assess honest signals? A take-home assignment could be easily ghost-written or AI-generated, while a live examination with plenty of prep time might encourage over-training / “memorising to the task”. A live presentation might bias students who are more accustomed to “winging it” and spontaneous *ahem* “expression on the spot”. We probably will need a range of these assessments. I hesitate to call this a “holistic” model but can’t think of a better term.
Parallel Session T2d: Assoc. Prof. Lisa Winstanley – AI in Art and Design Education
This session introduced some ethical issues in art and design, with a focus on AI and how it was trained, but didn’t really go into enough depth that I could say I learned something new. Fortunately the post-session panel discussion among the presenters was more engaging, and raised a good point about not being overzealous in forcing AI on students. They should have the option to say no, maintaining their agency and autonomy over how and when they would like to use AI.
Day 3: Learning Beyond AI (Knowledge Building and Assessment, Responsible Use, Ethics and Bias)
Keynote 5: Prof. Mairéad Pratschke – The New Context: The Pedagogical Imperative in the Shift from AI Tools to Systems
Prof. Pratschke attempted to introduce many concepts around AI use in a short time, making this session pretty overwhelming, but also one of the few sessions that sparked a lot of resonance.
AI presence—the way AI is presented to the user—is essentially the concept of affordances in design theory, which is also covered in the module on ICT in the postgraduate diploma programme I took over ten years ago. Affordances are the perceived and actual properties of an object that determine how it could possibly be used. A button affords pushing, a handle affords pulling, a chatbot affords conversation, an avatar affords representation, an agent affords acting on one’s behalf, a tutor affords teaching, and a digital human affords companionship.
The design implication is that form should match function and align with learning theory. A chatbot gives the impression that one is having a conversation; it encourages interactions and questions but also invites rebellion and pushing of boundaries, as teachers are aware students are wont to do. Is it always the most appropriate interface for learning? Even chain-of-thought reasoning, which some chatbots present to the user, can be misleading; different people/agents can arrive at the same conclusion through different reasoning paths, and the reasoning cannot be judged solely by its surface form (surely we have learned this from decades of multiple-choice questions, right?).
We need to be more deliberate in choosing the right AI presence for the learning activity.
The memory paradox had me nodding vigorously in agreement (in my head, so as not to disturb the session). Teaching is not merely transmission of knowledge; teachers who believe this tend to ask “if students can google/chatGPT this, why do we still need to teach it?” Even in the teaching syllabus we get, we can tell that some topics were not designed with procedural knowledge in mind: these topics tend to be presented as bullet points of facts students need to “understand”, which in practice ends up being memorization of declarative knowledge. Procedural knowledge requires practice—of what? What are the skills students need to demonstrate? Without clarity on this, we cannot design learning activities that build procedural knowledge.
Context engineering is a very handy term for what I have been trying to conceptualize for EdTech: a common (probably digital) space, not just a digital repository of learning materials. This space is where students co-create learning through interaction and collaboration, with teachers curating topic-appropriate material and facilitating discussions, while AI/edtech helps to maintain the space and environment and keep it conducive to learning.
Keynote 6: Prof. Phillip Dawson – Assessment Design for the AI Era
Prof. Dawson’s keynote did not introduce anything truly groundbreaking or surprising, which was exactly what I expected. There is very little that is truly novel in assessment design; what is surprising is when a school has the courage to actually implement these well-known principles.
So this session served more as an alignment check for what panelists and audience agree on: the need to teach learners when and how to use AI, to have multiple modes and formats of assessment (“swiss cheese” approach), to assess process not just product, to provide opportunities for collaboration (human-human and human-AI), and to apply security measures only at key milestones.
No disagreement there, only questions on how to make it happen in our social and institutional context. How would we design structural assessment that includes both AI-enhanced and non-AI-enhanced components? How do we train teachers to design assessments that truly assess learning, not just compliance with AI policies?
Panel Discussion 3: Prof. Mairéad Pratschke, Prof. Phillip Dawson, Mdm Lee Lin Yee (MOE), Mr Jason See (MOE); Chair: Assoc. Prof. Teo Tang Wee
The discussion raised many questions, not all of them that interesting I reluctantly admit. I found myself noding to Mdm Lee’s description of encouraging schools to “fail fast, fail forward” although I’m seeing how it is not really translating well on the ground yet. Lack of clarity around what “experimentation” means or looks like, leads to teachers being unsure of the difference between experimentation and failure, leads to … less / no experimentation. Teachers are aware that students have noticed some lecturers/educators using ChatGPT to answer questions, and frown on the practice; we tend to be wary of using AI in the classroom, in ways that sometimes hampers the experimentation mindset.
Mr. Jason See’s description of policy and technology as “seatbelts” was a good metaphor, but didn’t land for me; the current policies feel more like a wheel chock than a seatbelt, providing stability but limiting exploration rather than enabling safe movement.
By exploration I don’t mean “use ChatGPT, Perplexity, Gemini, Bard, Claude, etc” but figuring out ways to integrate context into AI tools. Understandably there are data governance concerns, but SLS as the only solution to concerns of data privacy is not sufficient if we want schools to truly experiment.
I think Prof. Pratschke was most spot-on when she somewhat obliquely mentioned that context and infrastructure determine what we can and cannot do. (On hindsight, I wonder if she intended this as commentary on the discussion?)
Parallel Session: Empowering Students in the AI Age—Prof. Venky Shankararaman, Vice Provost (Education), Singapore Management University
Sound principles; I like his way of encouraging AI adoption by making it difficult to accuse students of AI use. This process is often also unfair to students; there are many valid ways of using AI which to learners might not be considered cheating. But teachers are often reluctant to admit this potential misuse of the asymmetry of power. I think there is much to support this intention to add more “UX friction” to the plagiarism accusation process.
I don’t have much comment on the rest of it because it remains to be seen how well they are operationalized.
Parallel Session: Systemic Framework for Assessment & Curriculum Design—Dr. Ho Shen Yong, Executive Director of InsPIRE, NTU
I like the 2×2 framework, that was a really neat way to simplify decisions and check assessment coverage.
I am also in agreement that “critical thinking”, as a field in itself, is nebulous; each field has its own lenses, heuristics, and protocols for critical thinking. Attempting to teach critical thinking qua Critical Thinking, a cross-disciplinary skill, is likely to fail unless it can assume that learners have sufficient depth and domain knowledge in one or more areas.
Parallel Session (Science of Learning Research Showcase): NIE Team – Digital Portfolio Implementation and AI Reflection Assistant
No comment on this; it seems sensible, I have yet to meet begining teachers who have experienced this digital portfolio system (and so have no second-hand experience to share), and have not used this portal myself. It does seem like a useful tool for self-reflection and I may try it with my student leaders in a couple years’ time (because we don’t have that infrastructure in place yet).
Symposium: Dr. Lydia Cao (Chair), Assoc. Prof. Bodong Chen, Dr. Teo Chew Lee, Dr. Katherine Yuan – Knowledge Building with AI
This was a very interesting session, and way out of my depth and circle of experience. Knowledge-building sounds like a powerful pedagogy, if one is in an educational context set up for it, and we in Singapore are not.
I do appreciate the similarities between Knowledge Building and what I am conceptualising for constructivist learning environments in EdTech: both emphasise learner agency, co-creation of knowledge, and collective advancement of understanding.
Closing Keynote: Prof. David Hung & Assoc. Prof. Tan Seng Chee – Learning vs. Performance Augmentation
This closely mirrored what had been mentioned earlier, particularly by Prof. Cukurova and Prof. Dawson. The main highlight was a distinction between AI for productivity (performance) and AI for learning (learning), which I think is a useful framing for educators to consider when deciding how to integrate AI into their teaching practice.
Professionals in most other fields are looking for productivity gains; educators should be looking for learning gains. The affordances required from AI tools in each case are different, and the design principles for integrating AI into learning activities should reflect this difference.
[End of Part 2]
Part 3: My view on EdTech and AI in the next five years
It took me a few days to synthesize my thoughts and ideas on a way forward after the conference.
For context, I teach H2 Computing in a junior college in Singapore. Our students go through a two-year pre-university programme that prepares them for studies in an undergraduate degree programme, and culminating in the General Certificate of Education, Advanced Level (GCE A-Level) examination. Our teaching curriculum covers programming in Python (up to object-oriented programming), data structures and algorithms, databases, networks, and web programming.
In addition to that I also started a computing talent development programme this year, gathering students to conceptualize applications and services for the college by applying design thinking principles and software development practices.
EdTech Masterplan 2030
To begin this review with some context, the Ministry of Education unveiled its EdTech Masterplan 2030. Not explicitly mentioned is a strong push towards AI integration in education. However this was formulated, in staff meetings this year, I felt it as a strong directive to adopt generative AI (genAI) tools in teaching and learning, but with little actionable guidance around how to do so, and why.
The masterplan mentions 3 key enablers needed to enable implementation:
- Key enabler 1: Learning analytics and data
- Key enabler 2: EdTech infrastructure and solutions to support school processes and meet rapidly changing needs
- Key enabler 3: EdTech ecosystem
I’ve seen all three play out on Student Learning Space (SLS), the national learning platform for Singapore schools. Its primary focus appears to be delivery of micro-lesson units for primary and seconday schools, with some AI features such as a feedback bot that provides feedback on simple responses. From my exploration so far, its suitability for higher education (junior college level and above) appears limited. Thats fair; pre-tertiary students make up the minority of the student population, and given their age and maturity are expected to manage their own learning somewhat more successfully. Understandably, they are not the most urgent user group to tackle.
Nonetheless, if we are expected to adopt genAI tools in our teaching practice, these three enablers need to be in place to support us as well. It is through the lens of these three enablers that I will review the conference sessions I attended.
Learning analytics and data
The learning analytics and data currently available through SLS primarily focuses on tracking student progress through lesson units, and response accuracy. In AIFE 2025, I saw many presentations exploring other forms of learning analytics, with many presenters recognising their limitations.
Dr Ho is exploring process telemetry: looking at number of edits, rate of edits, pause times and duration, and other process-oriented metrics to better understand student learning not just as an outcome but as a process. Dr Arif is exploring automation that can connect student feedback and improvement across linked lesson units; has this student improved on or addressed shortcomings from the previous assignment?
This level of telemetry and cross-assignment tracking enables more sophisticated analyses of the kind that usually requires an attentive teacher who has noticed that Bobby has been staring at the screen without typing for the past 5 minutes, or Tim has been going over the same paragraph multiple times without meaningful changes, or Sarah has been repeating the same mistake across 3 assignments. It can detect confusion or anxiety as it happens / if it happened, regardless of the polish in the final product, and thus unlocks the possibility of real-time interventions, or a discussion on the socio-emotional experience the student underwent.
Ironically enough, telemetry and cross-site tracking are jargon that were introduced in online advertising when it took off in the early 2000s; the engagement farming industry takes user engagement so seriously that they have jargon for discussing what their online tools do in online advertisements and social media apps. ChatGPT could give you the low-down on what these terms mean, how to measure them, and how to optimise for them; the closest equivalent it could give me for the education sector is “learning analytics” or “learner trajectory” (I had to ask ChatGPT because I don’t recall coming across any equivalent term in all my years of teaching). You would, I hope, agree that these terms do not have the same level of precision.
That’s the crude understanding and low model fidelity readily available in today’s widely available AI models. It’s easy to understand why: these processes have been carried out intuitively and subconsciously by the best teachers, with little need to explain it to this level of detail. We don’t need to explain in such detail because we primarily train other human teachers whom we can expect to share the same intuitions. The advertising industry had to conceptualise, define, and then jargonise these terms only because they now need to be codified into advertising and engagement models; I suspect the education sector will soon have its moment when it needs to “explain to AI” how teaching happens. And it will need to do so to develop similar capability for education.
I can already give this kind of meaningful, engaging feedback for 1–3 students in a small group setting. To do it for a class of 20 I need these attention-scaling tools.
EdTech infrastructure and solutions
On the lack of teaching and learning tools
The “strong directive to use genAI tools in teaching and learning” I mentioned earlier manifests on the ground as frequent, strong reminders to explore the use of genAI tools in lesson planning, classroom activities, and assessments, but with little explanation of why or how. In practice that looks a lot like:
A: “Have you tried using ChatGPT to generate some quiz questions for your next lesson?”
B: “Yes, I uploaded some past year papers and prompted it to generate questions for <topic>.”
A: “You uploaded what?”
or
A: “Have you tried using ChatGPT to improve student feedback?”
B: “Yes, I copy-paste their answers into ChatGPT and ask it to give feedback, and sometimes I also tell it more about the student so it gives more sensitive and empathetic feedback.”
A: “You told it what about the student?”
While we are still undergoing trial periods with (limited versions of) Copilot and Gemini, which experientially feel like using ChatGPT with the same amount of copy-pasting involved, there are few other platforms or tools being offered to educators to meaningfully integrate genAI into their teaching practice. If I talk to 5 other educators, I hear about 5 different genAI tools being trialled; three different AI voice generators, two different AI video generators, five different summarizers, and even more custom chatbot platforms.
Everyone is hunting, not for the best tool or even the right tool, but mainly for a free tool; with any luck this tool remains free for the next 1–2 years before they sink and we scramble for another free tool—raising serious data governance questions each time—or the company finds product-market fit and starts monetising, forcing us to migrate again with the same unresolved data concerns.
On the lack of infrastructure
For our daily drivers we’re stuck with Pair Chat, a government-provided, Claude-powered, second-wave chatbot, and whatever AI features get integrated into SLS over time, as the only officially-sanctioned platforms for anything involving potentially sensitive student data.
These are individual solutions, rather than integrated infrastructure. Infrastructure would be a platform that allows educators to plug in different genAI models or tools as they see fit, and tools for building genAI-powered applications beyond chatbots and prompt pasting. This usually looks like an API in early stages, and a marketplace/plugin registry in later stages. Most importantly, a robust data governance framework with accompanying tools for handling student data securely and ethically, such as data anonymisation, consent management, and audit trails. Without these, educators get laden with more checklists and cognitive load about what data can be used where, and whatever productivity gains we get are squandered away doing compliance work manually.
At the talks I attended, I hear educators using existing models, but not platforms. They have had to develop their own solutions, and they see infrastructure as the next question mark they have to tackle. Prof. Peter Johnson (Imperial College London) came to present Lambda Feedback, a platform for connecting AI micro-services to quiz platforms, and look for collaborators to work on a common API schema for the microservice, because one doesn’t exist yet.
We cannot rely solely on vendors or the EdTech industry to build these for us. We need to take an active role in building or co-developing them, because we know our needs best.
On the purpose of EdTech
I am told that the adoption of genAI is primarily focused on improving teaching and learning (TnL), less on improving productivity for administrative tasks. I suppose the idea here is that rather than directly reducing teacher workload, genAI is expected to enhance the quality of TnL activities.
Unfortunately, this puts the cart before the horse. Context management is administrative workload: collating assessment results and feedback, assigning and closing quizzes, tracking student progress, managing learning resources, and so on. How does a TnL tool work without this context? How does AI analyze student results without those results being copy-pasted or uploaded to it in the first place? How does AI generate personalised learning paths without knowing what the student has done so far? How does AI create lesson materials without knowing the curriculum and learning objectives?
Perhaps when upper management visits an AI-enhanced classroom and sees students engaging with AI chatbots, collaborating with each other through AI-mediated discussions, and receiving AI-generated feedback on their work, that’s the vision burned into their minds: This! We want more of this!
There’s a lot of behind-the-scenes work that needs to be done for this to happen. That work is largely administrative work. To see more of the former, you’ll need to invest in reducing the latter, making it easier or faster to do, or better yet, outright reconceptualise how data and context are managed so that it might not even be necessary to do that work.
EdTech ecosystem
Locally, the EdTech ecosystem is still nascent. There are few local EdTech startups, and even fewer focusing on AI in education. As interesting as some of these educational platforms and tools are, they often do not align with local educational goals, curricula, or cultural norms; researchers cannot afford hosting these solutions for local schools, schools often don’t have enough funding to pay for a vendor to implement these solutions, and even when they do, the solutions are often existing products with some adaptation. Without startups or local companies building solutions from the ground up for local needs, the ecosystem remains fragmented and underdeveloped.
Currently, we face a choice between building everything in-house or adopting overseas solutions that may not align with local needs. The middle ground—co-developing contextually relevant solutions—remains underdeveloped.
This is not to complain that nothing is being done. Excellent work is being done to build some sorely needed tools; I particularly enjoy and am inspired by Chan Kuang Wen’s extensive work building semantic search and mentoring students working in this area. But when I ask myself where I see EdTech in the next 5 years … I think it’s got to be more than a smarter search box, another chatbot with customisable prompts, or a more contextually aware quiz generator or feedback bot.
I think the EdTech folks working on these individual pieces should continue doing that, but I don’t think that is where I will best contribute. I want to spend the next five years working on the infrastructure that connects all these pieces together.
As Prof. Pratschke put it, we need to move on from prompt engineering to context engineering. If we’re not careful, instead of maggot therapy that eats away the non-human parts, we might end up with Frankenstein’s monster, a patchwork of disconnected AI tools that don’t quite work with each other, leaving the human to do the inhuman gluework of copy-pasting prompts, downloading and uploading documents, and sieving out personal data manually.
EdTech and AI in the next 5 years
Prof. Peter Johnson of ICL is trying to collaborate on AI microservices; Dr Ho Jia Xuan is experimenting with telemetry tools for writing; more than a number of names are working on context-aware bots. We need more of these efforts, ideas, and projects; AI for learning and AI for production have different goals and therefore needs. In 10 minutes, I came up with some missing pieces:
- UI components for building AI-infused apps
- SDKs for data handling and model selection
- Specs and schemas for data exchange and interoperability
- Evaluation and test suites for AI learning tools
The last and, in my opinion, most important piece is the platform: I can’t yet imagine what a universal learning platform would look like, but I can imagine and ideate what might work for my college.
More importantly, I need to pull together a team interested to explore, build, and test this platform; this work cannot and should not be done alone. If, as Dr Pratschke puts it, “AI in learning is all about context, context, context”, then something needs to be managing that context, and a cohesive context manager is going to end up being a platform for all of the above.