The Age of AI Requires a New Kind of Education

Conducting 30 oral exams with first-year international students last Thursday was a massive eye-opener. Turnitin flagged over 50% of their assignments as AI-generated—some hitting a full 100%. Even though the assignment brief explicitly stated "no AI," most students admitted using it for translation, editing, or proofreading. It turns out they are far more terrified of failing than they are of an academic misconduct charge.

This highlights an embarrassing situation after more than two years of struggling with AI in higher education: there is still a lack of awareness around responsible AI use, combined with a surprisingly low capability in using it effectively among students. When I polled another group of international students 18 months ago about their using of AI in learning, the results were the same: most just used AI as a basic search engine or a quick translation or editing tool, just like these 30 students. Student AI literacy is lagging far behind the actual speed of tech evolution.

Our students are not ready for the Age of AI because our education system is not ready. So, how do we fix this?

First, we need to integrate foundational workshops on AI tools, prompt engineering, and digital ethics straight into day-one induction sessions.

Second, we have to weave responsible AI use directly into our teaching and assessments. Many educators have already started experimenting with AI-integrated pedagogy and assessment, including me. In my own research methods module, students must develop a publication-level research proposal, explicitly declare their AI usage, and critically reflect on it using a human-in-the-loop framework. Interestingly, the trend has shifted: while students used to fail due to the irresponsible use of AI, they now fail primarily due to its insufficient and ineffective application.

We can't just keep fearing, debating, and policing AI. Now is the time we have to teach students how to master it.

Institute's Reflection: Designing Learning That Develops Human Judgment

This perspective highlights an important shift in the conversation about AI in education. The challenge is no longer simply whether students use AI, but whether they know how to use it in ways that support learning. Research increasingly suggests that AI literacy extends beyond technical skills. It also includes understanding AI's capabilities and limitations, critically evaluating its outputs, using it ethically, and knowing when human judgment should take precedence (Ng et al., 2021).

The observation that students often rely on AI for translation, editing, or generating quick answers reflects a broader challenge identified in learning research. While AI can reduce the effort required to complete tasks, learning occurs only when students remain cognitively engaged. Research on self-regulated learning shows that meaningful development depends on planning, monitoring, reflecting on, and evaluating one's own thinking rather than outsourcing these processes (Zimmerman, 2002). Producing an answer is therefore different from developing the knowledge and skills needed to solve similar problems independently in the future.

The emphasis on prompt engineering is equally important. Recent experimental evidence suggests that structured prompting can encourage users to engage more deeply with problems, consider alternative perspectives, and strengthen critical reasoning rather than simply offloading thinking to AI (Gerlich, 2025). In this sense, learning effective prompting is not only a technical skill but also a cognitive practice that can support deeper learning when combined with reflection.

A human-in-the-loop framework illustrates this shift well. Asking students to explicitly disclose and reflect on how they use AI encourages them to remain active participants in the learning process rather than passive users of AI. This also suggests an evolving role for educators. Rather than focusing primarily on detecting AI use, they may increasingly need to adopt coaching principles that encourage questioning, reflection, experimentation, and personal responsibility for learning. Evidence from coaching literature shows that it is particularly effective in supporting behavioral change, self-reflection, and the development of new ways of thinking and working (Nicolau et al., 2023). Similar principles could help students move from simply using AI to learning with AI.

Our own Sense-Making Labs reached a similar conclusion. Participants emphasized that AI creates the greatest value when it acts as a thinking partner rather than a shortcut to answers. Developing AI literacy, therefore, requires more than teaching prompt writing. It involves cultivating curiosity, critical thinking, ethical judgment, and reflective learning habits that enable students to continue learning long after the technology itself evolves.

References:

• Gerlich, M. (2025). From offloading to engagement: An experimental study on structured prompting and critical reasoning with generative AI. Data, 10(11), Article 172.

• Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509.

• Nicolau, A., Candel, O. S., Constantin, T., & Kleingeld, A. (2023). The effects of executive coaching on behaviors, attitudes, and personal characteristics: A meta-analysis of randomized control trial studies. Frontiers in Psychology, 14, 1089797.

• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.