Education in the AI Era: From Policing Cheating to Redesigning Learning
Cheating cases tied to tools like ChatGPT on recent midterms have pushed a familiar debate to a breaking point. Some say AI makes students lazy. Others see it as a catalyst for better questions and stronger critical thinking. Both sides are right-and that's the signal: our model of teaching and assessment needs a reset.
Redefine Goals and Roles
At the 3rd Taejae Future Education Forum 2025 in Seoul, leaders were blunt: AI now delivers formal knowledge better than humans. The old approach-mass delivery of major-specific content-doesn't hold up anymore.
In a keynote, University of Toronto's Rotman School professor Mina Moldoveanu noted that it's getting hard to know who did the work-students or AI-and that many students are ahead of faculty in AI use. The takeaway: the current evaluation system is failing. We need a framework that trains students to ask sharper questions and reinterpret AI output with rigor.
- Center learning on question generation, not answer recall.
- Require students to show their reasoning and cite the basis of claims.
- Use AI as a conversational coach that probes logic and gaps.
- Shift faculty roles from lecturer to facilitator of thinking.
Assess What Matters in an AI-Heavy Classroom
Future evaluation should track how students respond to problems and develop logic through dialogue with AI. Don't grade the final answer alone. Grade the process.
- Open-AI exams with constraints: students disclose prompts, models, and settings.
- Oral defenses and mini-vivas to validate understanding.
- Versioned submissions: draft → AI-assisted revision → reflection on changes.
- Require claim-evidence-warrant structures, even for AI-generated text.
Learning Analytics: From Measurement to Action
China is deploying AI-based learning analytics across higher education. Systems track the impact of teaching methods and surface each student's achievements, misconceptions, and weaknesses. The message was clear: ignore this shift and you risk losing international competitiveness.
- Start with a pilot: pick two courses with clear learning outcomes.
- Use existing LMS data first; add AI models only where they improve insight.
- Build simple dashboards for instructors and students-actionable, not flashy.
- Stand up an ethics review: data minimization, opt-outs, bias checks.
Educational Administration Is Changing Too
In a keynote on the "A.G. (After GenAI) Era," Paul Kim highlighted AI coaching systems for personalization and career planning. He argued for centering curricula on fundamental question generation and the 6Cs: communication, critical thinking, creativity, collaboration, empathy, and commitment.
Tan Seng Chee described "agentic AI" that supports advising and course design. Think of AI that guides course selection across a student's path, and builds lecture materials and teaching guides aligned to an instructor's style and needs.
- Deploy AI advising with clear boundaries: what it can and cannot recommend.
- Use AI for course design drafts; keep faculty as the final editor.
- Offer short, recurring faculty upskilling on prompts, critique, and policy.
- Log interactions for quality control and student privacy audits.
Academic Integrity: Move From Detection to Design
Cheating is a design problem. Detection alone won't scale. Update policies so acceptable AI use is crystal clear-by assignment and by outcome.
- Define allowed AI uses per task (ideation, editing, code comments, none).
- Require disclosure: model name, prompts, key outputs, and what was kept or changed.
- Assess transfer: new problems, oral checks, or in-class performance.
- Provide equitable access to approved tools and accessibility supports.
For policy scaffolding and ethics, see guidance from UNESCO and the OECD.
The First 90 Days: A Practical Plan
- Weeks 1-2: Form a cross-functional AI task force (faculty, students, IT, legal, IRB). Set principles and scope.
- Weeks 3-4: Publish an interim AI-use policy and an instructor toolkit with sample rubrics and assignment templates.
- Weeks 5-8: Run two pilots-one assessment redesign, one AI advising. Collect consented data and feedback.
- Weeks 6-10: Deliver micro-trainings for faculty on prompts, critique, and process grading. Offer office hours for course makeovers.
- Weeks 9-12: Review impact and risks. Iterate policy. Plan scale-up for the next term with budget and owners.
Why This Matters
AI won't replace good teaching. It will expose weak design. If we focus on questions, reasoning, and agency, students learn more-whether they use AI or not.
Looking for structured upskilling paths for educators working with AI? Explore practical options here: AI courses by job.
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