More Than Just a Bot: How a Simple “Engagement Formula” Brings the Human Touch Back to Learning

Introduction: AI in Higher Education
For many years, when studying at a tertiary institution, the deal was simple: a lecturer gave students an assignment topic, and they wrote back what they had memorised.
In recent years, with the onset of Artificial Intelligence tools such as ChatGPT and CoPilot, this deal has basically been shredded. It has become a very easy task to provide any AI tool with a prompt, asking for a summary or a standard essay, and, within seconds, you have that summary or that essay written in the language of your choice and the writing style provided in the prompt.
This has led to the question of whether any student is actually learning something, or if they are simply regurgitating or proffering what AI provides them with.
By letting AI tools take over our ability to think critically and with discernment, leads to a weakened ability to gain insights. The solution, unfortunately, is not to try to play detective and use AI-checkers (which, as many before us have realised, may provide false positives or false negatives, and therefore is not a reliable tool).
The solution lies in changing the way higher education institutions teach, to move the focus away from the final examination paper that needs to be completed, to the actual journey the student takes to reach the final examination or assessment.
Making Tasks “AI-Resistant”
For a higher education institution to not only survive in the AI era but also to continue providing authentic assessments and ensure academic integrity, the assessment tasks need to be changed to become either AI-resistant or AI-conscious.
Assessment tasks need to be designed to reward human creativity and problem-solving, and should not be designed to just look up facts or ask ChatGPT to provide an answer.
It was with this background that Prins and Bell (2025) presented a paper at the IIE 2nd International Conference on Teaching and Learning, held in Cape Town at the end of November 2025.
Their research paper argues that academics need to move toward tasks that an AI bot can’t easily complete.
Examples of AI-Resistant Tasks
Based on the evidence, these tasks usually involve:
- Real-world projects: Solving actual problems or case studies.
- Reflective journals: Writing about the student’s own thoughts on AI-generated ideas.
- E-portfolios: Showing how the student’s work grew and changed over time.
- Face-to-face chats: Oral exams or role-plays where the student has to think on their feet.
By following this approach, the focus shifts from the “what” to the “how”, thereby ensuring the work reflects a student’s own thinking rather than a polished, AI-generated output.
This approach also encourages critical thinking, problem-solving and creativity, which are critical skills for the future world of work.
The 83% Rule: What Actually Makes Students Care?
Their study evaluated 67 undergraduate modules to ascertain what really gets students truly engaged in their work. While “engagement” is often seen as a vague concept, they found that 83% of student engagement comes down to just two main things:
- Practical Work (55%): Putting in the effort on hands-on, real-world tasks.
- Independence (28%): Having the freedom to choose how they work and who they work with.
The biggest driver is Practical Engagement. Students “lean in” when a task feels like something they will actually do in their future careers. They believe that when they are busy solving a real-life challenge, they remember things better and learn how to adapt that knowledge to new, difficult situations.
Students move from just trying to “pass” to actually wanting to “master” the skill.
The second factor, Autonomy (or Independence), showed something interesting about working in groups. Students in the study didn’t see “working with peers” as boring or difficult; instead, they saw it as a form of freedom.
When students get to choose their own roles in a team and figure out how to solve a problem together, they feel more in control. In this setup, teamwork is not just an assignment—it is a choice they make to get the job done.
Interestingly, the study found that just telling or instructing a student to “be creative” does not suffice on its own. Creativity needs a solid foundation of practical use and personal choice to work.
The institution cannot expect a student to be inspired if the task does not feel relevant or give them any real say in the matter.
The Proposed Game Plan for the Modern Classroom
- Use Real-world Tasks: Utilise case studies and simulations that mimic the workplace.
- Give Students Choices: Let students select their own topics or how they present their work. Choice fuels motivation.
- Improve Group Work: Let teams manage themselves and hold each other accountable.
- Focus on the Process: Give formative feedback on the drafts and the improvements, not just the final mark.
- Use AI Properly: Do not ban it—teach students how to use it responsibly for brainstorming ideas, then let them critique what the AI tool produced.
Conclusion
The AI “scare” in higher education might be the best thing to happen to teaching. It has forced educators to change the way they teach and the way they assess. By focusing on practical work and student independence, AI can be used as a tool to enhance deeper thought and more critical thinking.
By giving students real-world challenges that matter to their future and the freedom to tackle them their own way, makes their learning more human and relevant. An essay written merely by AI proves rather pointless.
Reference List
Prins, R., and Bell, B., 2025. The ‘Practical Engagement’ and ‘Autonomy’ Factors Underlying Student Engagement in AI-Resistant Assessments, IIE 2nd International Conference on Teaching and Learning, Cape Town.