India's B-Schools Embrace AI, but Only 7% of Faculty Are Experts

AI in Indian B-Schools: What Faculty Are Doing, What They Think, and What You Should Do Next

A new MBAUniverse.com survey of 235 faculty across leading Indian B-schools (IIMs, IITs, ISB, XLRI, SPJIMR, MDI, NMIMS) shows steady AI adoption-with clear gaps in skills, policy, and assessment.

Key findings at a glance

• Impact on learning: 51% see a positive effect; 21% say it's too early to judge; 18% report a negative effect; ~10% see no clear impact.
• Faculty proficiency: 55% identify as intermediate users; only 7% as experts-signaling a need for structured upskilling.
• Where AI is used: Most in research and teaching; curriculum design is rising; admin and assessment are still early-stage.
• Tools for teaching relevance: ChatGPT leads, followed by Microsoft Copilot and Perplexity; Gemini and Claude are moderate; Meta AI ranks lowest.
• Top challenges in research use: Ethics/integrity concerns, unreliable outputs, and missing regulatory guidance.
• Outlook: Over half expect AI's role in teaching, curriculum, and research to grow in the next 12 months.

Why this matters for academic leaders

The gap between usage and expertise is widening. Faculty are experimenting, but support systems-training, policies, assessment frameworks-lag. Without them, you get uneven outcomes: time saved in research, but confusion in grading standards and academic integrity.

As stated by the Department of Higher Education, AI should remove barriers of language, background, and geography, giving every student the freedom to ask questions. That goal requires clear rules and targeted capacity building, not ad-hoc pilots.

Action plan for the next 90 days

• Set guardrails: Publish a short AI use policy for teaching, research, and student work. Clarify what's permitted, what must be disclosed, and consequences for misuse. Reference national guidance where relevant (for example, Department of Higher Education or Responsible AI for All).
• Upskill fast: Run tiered workshops: fundamentals for beginners; prompt strategies and evaluation for intermediates; workflow automation and research methods for advanced users. Make completion visible in performance reviews.
• Modernize assessment: Move to process-based grading: proposal → drafts → reflection → oral defense. Require disclosure of AI use and include AI-detection only as a secondary check, not the primary decision-maker.
• Standardize toolkits: Approve a core set (e.g., ChatGPT, Copilot, Perplexity) with clear use-cases. Provide alternatives where data sensitivity is high.
• Elevate research rigor: Require method notes that specify prompts, models, parameters, and verification steps. Integrate human evaluation rubrics for AI-assisted outputs.
• Create a faculty AI desk: A small cross-functional team to answer queries, review tricky cases, and update policies every semester.

Practical guidance for faculty

• Teaching: Use AI to draft case variations, generate discussion prompts, and produce quick summaries-then layer your context and counterpoints.
• Curriculum: Add AI literacy modules: prompt craft, fact-checking, bias review, and results comparison across tools.
• Student work: Require an "AI use statement" on submissions describing tools used, prompts, and verification steps.
• Research: Use AI for literature mapping and code stubs, but verify citations and reproduce key results. Keep an audit trail of prompts and outputs.

What leaders are saying

According to the Department of Higher Education, AI can strengthen both teaching and learning by widening access and encouraging inquiry. Another industry voice noted that AI is set to transform business processes, jobs, competencies, and higher education-management education sits at the frontline of this shift.

Tool insights from the survey

• Most relevant for teaching: ChatGPT
• Next: Microsoft Copilot, Perplexity
• Moderate: Google Gemini, Claude
• Lowest: Meta AI

Risk management checklist

• Ethics and integrity policy with disclosure rules
• Verification workflows to catch inaccuracies
• Clear stance on sensitive data and model selection
• Periodic reviews of tool performance and bias

For deans and program chairs

Treat AI like any core pedagogy upgrade: set standards, train your people, align assessment, and monitor outcomes. The data shows interest and momentum. Your job is to convert it into consistent student learning gains and credible research.

Next step: structured capacity building

If your faculty need a curated path to skill up by role and proficiency, explore targeted programs and certifications. Start here: AI courses by job.

Source: MBAUniverse.com faculty survey across India's leading B-schools; report released at the 15th Indian Management Conclave.