Report: MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training

Date: August 13, 2026

Following a rigorous five-month period of research, community outreach, and deliberation, the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training has concluded its investigation. Our goal was to analyze the integration of generative AI within the Institute's academic ecosystem and provide a roadmap for managing the resulting tensions and opportunities.

The Official Charge In January 2026, Faculty Chair Roger Levy, Provost Anantha Chandrakasan, and Chancellor Melissa Nobles tasked the committee with a primary objective: Assess the current state of AI utilization at MIT.

While we began with that specific mandate, the committee quickly realized that the conversation needed to expand. We concluded that the MIT community—particularly the faculty—must engage with more fundamental inquiries regarding the essence, structure, and intrinsic value of an MIT degree in an era where AI is a primary disruptor.

Committee Composition

The group was designed to be inclusive and multidisciplinary:

Despite our diverse backgrounds and the absence of a preconceived agenda, our findings converged into a unified perspective. This report serves to:

1. Analyze the current educational environment at MIT.
2. Present eight guiding principles for the Institute's future.
3. Propose immediate and long-term strategic actions for administration and educators.

MIT is uniquely positioned for this challenge. As a pioneer in AI's inception and a champion of rigorous, "learning-by-doing" education, we must lead. We aim to uphold the philosophy of Mind, Hand, and Heart, ensuring that residential education remains a human-driven endeavor designed for the $\text{betterment of humankind}$.

Signed, Eric Klopfer (Co-chair) & Sam Madden (Co-chair) On behalf of the Committee on AI Use in Teaching, Learning, and Research Training

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1.1 The Landscape

Our recommendations are rooted in the following observations regarding the current state of the Institute:

The Emotional and Professional Spectrum

Generative AI has permeated the campus, creating a wide variance in perspectives:

While AI fosters innovative pedagogy and student experimentation, it has also triggered several stable volatile effects on campus life:

• Disruption of Core Pillars: Traditional elements like the "p-set," take-home examinations, UROPs, study groups, and office hours are being upended.
• Fractured Trust: The "social contract" between the educator and the learner is eroding.
• Cultural Conflict: Decades of norms regarding personal integrity, collaborative problem-solving, and the "creative friction" necessary for deep learning are under threat.

The Need for a Strategic Response

The impact of AI is both incremental and seismic. We have identified several critical needs:

• Clarity: Students are currently struggling with a lack of consistent guidelines and justifications for AI use across different subjects.
• Curricular Evolution: Every subject must be audited to ensure that teaching methods, learning objectives, and assessments are AI-aware.
• Philosophical Shift: We must move beyond subject-specific changes to a holistic reassessment of the purpose of higher education.

Practical and Global Implications

The committee identified several high-priority concerns:

It is important to note that while other global institutions are facing similar dilemmas, there is currently no universal "gold standard" for solving these issues.

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Terminology Guide

To ensure clarity throughout this document, we use the following definitions:

• Instructors: Includes faculty and all personnel involved in teaching.
• Students: Refers to both graduate and undergraduate students (unless specifically mentioning GIRs or UROPs).
• We/Us/Our: May refer specifically to the committee members or to the MIT community at large.

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Guiding Principles

We have established eight principles to steer the Institute. The first is a call for intellectual modesty:

Principle 1: Be Humble

We must recognize that not all technologies integrate into society at the same speed.

The Gradual Model: Some tools evolve alongside society, allowing for a slow, mutual adaptation. The original computer—the foundation of AI—followed this trajectory: $\text{Technology} \rightarrow \text{Societal Adaptation} \rightarrow \text{Integration}$

The Abrupt Model: Other innovations arrive with a "shock" effect, becoming socially dominant before institutions can react. By the common definition—the moment a technology becomes accessible to the masses—generative AI was "born" with the launch of ChatGPT in late 2022.

def technology_impact(type):
    if type == "gradual":
        return "Mutual Adaptation"
    elif type == "abrupt":
        return "Institutional Shock"