Only 7% of Indian B-school faculty are expert AI users: What leaders should do next
A new MBAUniverse.com survey of 235 educators from IIMs, IITs, ISB, XLRI, SPJIMR, and others shows a clear gap: just 7% of faculty consider themselves expert users of AI tools.
Adoption is moving, but capability is lagging. That gap will shape curriculum quality, research output, and student outcomes over the next year.
Adoption is rising, confidence is split
Only 51% of faculty are confident AI will have a positive impact on B-school students. The rest are neutral or uncertain.
Even so, most expect AI to play a bigger role in teaching, curriculum design, and research over the next 12 months. The intent is there; the enablement is not.
Where AI is used today
Faculty use AI primarily for research and teaching. Usage in curriculum development is growing, while administrative work and assessment are still early.
- Research: literature synthesis, coding assistance, draft generation, data prep.
- Teaching: lecture planning, examples, case prompts, formative feedback.
- Curriculum: updating modules, creating practice sets, aligning competencies.
- Admin and assessment: rubrics, feedback templates, grading aids (emerging).
Tool preferences and guidance needs
ChatGPT was rated the most relevant tool for teaching-related activities. Interest is strong, but faculty want clearer training, toolkits, and policy guardrails for responsible use.
For context on the tool and responsible practice, see ChatGPT and UNESCO's guidance for AI in education here.
Impact and concerns
Most faculty see positive effects on learning, but 21% say it's too early to tell, 18% report unfavorable impact, and nearly 10% see no effect.
Top research-related challenges: ethical concerns, inaccurate or unreliable outputs, and gaps in regulatory policy.
Action plan for deans, chairs, and program directors
- Set a baseline: audit faculty AI skills by department; target a measurable uplift in 6-12 months.
- Publish a clear policy: disclosure rules for AI use, academic integrity standards, data privacy, approved tools, and citation expectations.
- Upskill fast: run peer-led labs, create office-hour clinics, and sponsor micro-certifications focused on prompt quality, fact-checking, and evaluation.
- Standardize toolkits: provide institution-managed access to core models and plugins with logging, content filters, and reference-check features.
- Embed AI in assessment design: require method transparency, source attribution, and process logs; assess reasoning, not just output.
- Back research use: define ethical review steps for generative AI, set reproducibility norms, and create a shared repository of vetted prompts and workflows.
- Measure outcomes: track learning gains, time saved, and research throughput; run A/B pilots and iterate.
- Risk controls: mandate fact-check passes, citation verification, and bias reviews; use plagiarism detection and watermark/scenario testing where appropriate.
A one-year roadmap
- Q1: Skills audit, policy draft, tool access, pilot workshops.
- Q2: Course updates with AI-enabled assignments, research workflow guidelines, ethics review process.
- Q3: Scale faculty communities of practice, publish case studies and rubrics, broaden lab access.
- Q4: Evaluate outcomes, refine policy, budget for next-year capability growth.
Get faculty-ready with structured training
If you need a practical path to capability building, explore role-based programs and certifications that focus on teaching and research workflows.
- Courses by Job for educator-specific learning paths
- AI Certification for ChatGPT for classroom and research use
Bottom line: interest is high, expertise is thin. Close the gap with clear policy, focused training, and measured pilots-then scale what works.
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