Key Details
The AI in Education, Research and Skills Development Roundtable, held on 7 July 2026, brought university, industry and international participants together to discuss how AI is changing higher education. Its summary report identifies priorities for universities; it does not announce binding requirements.
Governance: Set an institution-wide AI policy, with more specific rules for teaching, assessment, research, procurement and data use.
Assessment: Test understanding through projects, oral examinations, portfolios and evidence of reasoning—not written output alone.
Research: Disclose material AI use and independently check AI-generated findings, data and references.
Skills: Make AI literacy—understanding AI’s uses, limits and risks—a foundation for graduates in every discipline.
Access: Pair AI adoption with support for students facing gaps in devices, connectivity, language access or accessibility.
Assessment Must Show What Students Understand
Generative AI can produce convincing assignments without showing how much a student has learned. The roundtable therefore favoured assessment that reveals reasoning, judgement and the ability to check AI outputs. Projects, oral defence and applied tasks are among the approaches it discusses.
AI can also help educators provide feedback or identify learning gaps. For decisions with high consequences — such as final grades, scholarships or disciplinary action —the report stresses human judgement, transparent criteria and routes for students to challenge decisions.
Researchers Remain Accountable for AI-Assisted Work
AI can speed up literature reviews, coding and analysis, but responsibility for the final research stays with the researcher. The report calls for disclosure when AI materially affects a study’s content or methods, documentation that supports reproducibility, and independent verification of generated citations and findings.
That makes governance more than a rule about whether AI use is permitted. Universities need common accountability principles alongside rules suited to particular tasks, from student assessment to research and procurement.
AI Literacy Extends Beyond Technical Degrees
The roundtable treats AI literacy as relevant to law, medicine, agriculture, business, the humanities and other fields, with the depth of technical training varying by discipline. Universities’ distinctive contribution is to combine tool use with subject knowledge, critical inquiry and ethical judgement.
The report also sees a role for shorter, quality-assured courses as workers update their skills. Such microcredentialscan complement degrees, provided employers can understand what was taught and assessed. At the same time, language, disability and affordability must be considered if AI is to widen educational access rather than deepen existing gaps.
Policy Relevance
Decision for Indian Higher Education | Why It Matters |
|---|---|
Set common rules, allow disciplinary differences | A university-wide policy can establish accountability for AI use; departments still need assessment and research rules suited to their work. |
Equip faculty to change assessment | Projects, oral defence and AI-critique tasks require teaching time and assessment capacity, not just a policy telling students when AI is permitted. |
Address unequal access | Students’ ability to use AI responsibly depends partly on access to tools, connectivity, language support and accessible design. |
Relevant Question for Policy Stakeholders: What support do Indian universities need to assess students’ own reasoning and teach responsible AI use across disciplines without widening gaps between institutions or students?
Follow the Full Report Here: ITU: AI in Education, Research and Skills Development Roundtable