These words and phrases – common among the younger generation – signal a broad cultural shift. The evolution of language can be witnessed alongside changes in habits, the use of tools and gadgets, and overall learner expectations among youth. To remain relevant, higher education teaching must evolve with similar agility.
New learning landscape
Students are learning in profoundly different ways today. Some are visual-first learners with a preference for multi-modal content over textual emphasis. Social learners tend to thrive in collaborative settings. Self-paced exploration is seeing a growth spurt – learners are moving seamlessly between microcredentials, massive open online courses and industry-driven problem-solving projects. These different styles seem to have a standard denominator, calling for highly personalised, tech-infused and on-demand learning.
A strong ally in these shifts is the proliferation of artificial intelligence (AI) and its component tools, such as generative AI – paving the way for rapid code generation, the instant production of essays and presentations, real-time summaries of bulk reading material, and immediate formative feedback.
On a positive note, these changes are driving institutional reforms in programme development, content creation, new forms of delivery and infrastructure aligned with modern learner requirements. Virtual labs, cybersecurity ranges and digital twins for engineering, coupled with AI-assisted teaching and learning, are on the rise.
On the flip side, institutions and educators who fail to change with the times could face wider digital fluency gaps between learners and outdated programmes, potentially making graduates unemployable.
Pedagogical shift
Shifting from conventional to more contemporary pedagogy must focus on developing understanding, flexibility, and the learner taking ownership of the learning process.
Rather than overloading students with slide after slide of information, educators must add value through interpretation and application while fostering a deeper sense of inquiry. Here, approaches like flipped classrooms, constructive solutioning and design thinking can play a role.
To cater to different learner styles, academics should move away from a cookie-cutter, one-size-fits-all mode to a universal design for learning (UDL) approach. UDL implies engaging with students in a variety of ways, for example through reading materials, videos, podcasts and prototype development, without compromising standards.
There also needs to be an emphasis on creating a co-curated experience between learners and educators. This includes co-designing project parameters, assessment options and content delivery.
Concepts such as learning contracts and choice boards are now emerging, allowing students to co-create shared outcomes in different domains.
Authentic assessments must move from periodic evaluation towards continuous feedback, particularly for smaller cohorts and postgraduate students. Tools like micro-polls, brief oral defences, AI-assisted draft feedback and dialogue-driven feedback could foster a growth mindset among learners.
In strategising these pedagogical shifts, institutions must be mindful of students’ diversity, cohort sizes and the desired learning outcomes at both programme and module levels.
AI here to stay
The use of AI must become explicit, teachable and accountable. AI literacy modules and short courses on prompting, cross-checking, research citation and ethical use are necessary.
Providing evidence of its use where AI is permissible is an option. Cross-examining learners by checking prompt logs and through oral questioning to assess comprehension can be administered. At the core is designing assignments, such as real-world problem-solving tasks and reflective logs, that cannot be fully AI-generated.
Assessments must be devised to make them difficult to game. Studios and design sprints are useful for tech and computing programmes, and these can be coupled with industry demo days. For selected programmes like architecture, visual effects and industrial design, one-off exams should be replaced with portfolio assessments over time.
For business and finance modules, real-world simulations that mimic corporate problem-solving can form the bulk of assessments. Final year projects can be community-led, driven by non-governmental organisations, small and medium enterprises, or even government agencies. This will allow learners to generate socio-tangible outcomes.
Next steps
Given the above, institutions must invest in teaching quality to meet new learning and learner requirements. Policies must evolve to incorporate changes in learner styles and expectations, particularly in addressing AI requirements.
Structurally, universities need more flexible learning spaces and agile timetabling, while offering extended collaborative learning spaces co-designed with industry. Overall, learners need to feel supported academically, socially, emotionally and mentally.
As language evolves, so must teaching. Let’s aim for students saying, “That class was sick” and “The vibe on campus is awesome”.
Prof Dr Murali Raman is the deputy vice-chancellor (academic development & strategy) at Asia Pacific University of Technology & Innovation (APU), where he oversees postgraduate and continuous education, focusing on executive training and consultancy. Passionate about training people, his niche areas include design thinking, coloured brain communication and emotional drivers, digital economy, crafting digital strategies and mindset change. The views expressed here are the writer’s own.
