The higher education sector has spent years calling for more future-ready, pedagogically-sound and digitally-confident educators. Yet, teaching development programmes for research postgraduates are often in an awkward position.
For many postgraduates, teaching is neither their primary motivation nor focus of study, even though a significant number are required by their departments to undertake tutorials, laboratory teaching or small-group facilitation. At the same time, research-intensive universities have seen substantial growth in postgraduate intakes, making large cohorts in teaching development programmes increasingly common.
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Programme structures, however, still tend to assume small-group interaction and intensive instructor mediation – assumptions that become difficult to sustain in large cohorts. Expanding provision indefinitely is neither sustainable nor practical, particularly as expectations around teaching effectiveness, student experience and accountability rise.
As a result, the misalignment between institutional constraints, educational aspirations and programme design is getting wider, particularly for research postgraduates who already regard teaching as peripheral to their academic trajectory. How, then, do we equip research postgraduates with the necessary teaching skills at scale, without diluting the quality of learning?
One example of how these misalignments can be addressed through design is our a compulsory postgraduate teacher‑development course, which the Center for Education Innovation at my university offers. The course enrols large cohorts and is required for graduation, making it a useful lens on how a programme can evolve to respond to both scale and limited learner motivation.
From information delivery to meaningful learning
Teacher development programmes frequently promote learner-centred, active and technology-enhanced teaching. Yet their own design often defaults to transmissive delivery of frameworks, models and readings that consume valuable face-to-face time. A more coherent approach starts from a different premise: if institutions expect future educators to create environments that support active, motivated and technology-enhanced learning, then teacher training itself must model those pedagogical principles.
The first shift in our course addresses over-reliance on content delivery. Instead of using valuable in-person sessions to explain pedagogical theory, core pedagogical ideas are restructured into online pre-class readings, short videos and short-paced activities that establish a shared conceptual foundation. We reserve face-to-face sessions for experience rather than explanation, with participants engaged in activities, simulations, discussion tasks and technology-supported interactions – just like the classrooms they are expected to build.
In a session on constructive alignment, participants experience two contrasting designs for “learning to drive”. One group receives a lecture-only lesson where the instructor explains steering, signalling and accelerating, but offers no chance to practise. Another group reviews a brief theory, watches a demonstration and the rehearses basic manoeuvres in a guided simulation. Experiencing these designs side by side makes retention, confidence and skill immediately tangible. Theory is not removed, but embedded and surfaced through structured reflection.
This experiential framing is particularly powerful for research postgraduates who may initially have limited interest in teaching. Experiencing learning design first‑hand helps them develop pedagogical intuition – an understanding of how motivation, confusion, feedback and engagement emerge from concrete design choices. Theory then becomes a lens for making sense of lived practice, rather than an abstract body of knowledge to be memorised for compliance.
Sustaining motivation and support at scale
Once learning is structured around experience, the next challenge is sustaining motivation and providing timely support in large cohorts. Gamification is frequently proposed as one of the possible solutions, but it is often flattened into points, badges and leaderboards that privilege competition over learning. Such design risks narrowing motivation to extrinsic rewards and discourage learners who are uncomfortable with competitive dynamics.
In response, our course reframes gamification around learner agency. Participants can earn digital credits, namely mCoins, through their engagement across online and in-class activities, and exchange these for pedagogically relevant benefits, such as additional instructor feedback, revisiting an overdue assignment or extended time for a final assessment. Rather than instructors attempting to offer personalised support at scale, gamification strengthens learners’ autonomy to manage their workload. It also helps them develop the flexibility to direct their own learning in ways that best match their needs.
AI-enabled tools are layered into this structure to extend support without undermining autonomy. We developed several chatbots in the course to handle routine administrative queries, and provided structured prompts that guide participants in tasks such as drafting learning outcomes and designing appropriate activities.
This expands instructional capacity without requiring a proportional increase in teaching time. We encourage participants to question and refine AI-generated outputs, reinforcing the idea that AI remains an aid to pedagogical judgment rather than a replacement for it. By embedding AI into the learning process, the course not only addresses issues of scale but also reflects the contemporary teaching and learning environments.
From compliance to practice – lessons for large-scale teacher development
Taken together, these design shifts – rebalancing theory and experience, agency-oriented gamification and bounded use of AI, address scale, learner motivation and resource limitation in pragmatic ways. Instead of aiming to personalise instruction for every learner, the course redesigns the system that allows participants to take ownership of their learning, including pace, needs and opportunities.
Several lessons may be applicable to academics facing similar challenges.
- Scalability is best treated as a design condition rather than a constraint to overcome. Blended learning structures, together with technology‑ and AI‑assisted elements, can expand instructional capacity in large cohorts by providing timely support, feedback and scaffolding without requiring proportional increases in teaching manpower.
- Teacher development works most effectively when it models the pedagogy it promotes. When theory is introduced through flexible online components and face‑to‑face time is reserved for experiential learning, participants are more likely to internalise student‑centred and technology‑enhanced practices.
- Learner motivation in large cohorts is closely shaped by how agency is designed into learning environments. Large cohorts do not necessitate uniform experiences; thoughtful, non‑competitive gamified systems can support self‑regulation and purposeful engagement.
- AI literacy is becoming an integral teaching and learning skill, not as a standalone topic but as useful infrastructure woven through planning, feedback and learner support.
These insights suggest that effective teacher development is not about delivering more pedagogical content, but about designing the conditions in which meaningful learning can occur at scale. When teacher training mirrors the classroom we hope future educators will create, even reluctant participants can begin to see teaching as a professional practice rather than an imposed obligation.
Ka Yan So is assistant education development manager (teaching and learning) at Hong Kong University of Science and Technology.
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