Five Common Myths About AI in Higher Education and What Our Research Actually Found

Cutting through the noise to help institutions make smarter AI decisions

As AI continues to reshape conversations across higher education, leaders are navigating an increasingly crowded landscape of competing claims, vendor promises, and institutional anxieties. In the midst of this information overload, misconceptions can cloud decision-making and slow meaningful progress.

Through our national study of AI adoption across 33 postsecondary institutions, conducted by T3 Advisory in partnership with Complete College America, we heard recurring myths that shaped how institutions approached (or avoided) AI integration. These misconceptions weren’t limited to skeptics; even enthusiastic early adopters held beliefs that risked stalling or derailing their efforts.

Below, we share five common myths we encountered and the realities our research revealed.

Myth 1: AI Will Replace Faculty and Staff

This fear surfaced repeatedly in our interviews, yet the reality tells a different story. AI is not a substitute for human expertise but a tool that can offload routine tasks and streamline processes. Faculty and staff connections with students remain essential for teaching, mentoring, and personalized support.

Institutions that framed AI as a collaborator rather than a competitor saw higher levels of engagement and more effective use cases. As one institutional leader put it, resistance often faded when employees saw the technology being used to support their work rather than eliminate their positions.

Myth 2: After the Initial Purchase, AI Will Save Money

While AI can create efficiencies, our research found that adoption requires significant upfront investment extending far beyond software licenses. Training, infrastructure upgrades, governance development, and ongoing system maintenance all demand resources, both financial and human.

Leaders who planned only for licensing costs consistently underestimated what sustainable adoption would require. Without accounting for these elements, institutions risk stalled implementation, fragmented adoption, or inequitable outcomes. The institutions making the most progress had folded AI into strategic budgeting conversations rather than treating it as an add-on expense.

Myth 3: AI Adoption Is Mainly an IT Issue

Treating AI as solely a technology problem emerged as one of the clearest predictors of fragmented implementation. Our findings show that AI adoption requires whole-institution readiness spanning governance, faculty development, student training, and policy frameworks.

Institutions with cross-functional working groups (including representation from academic affairs, student services, IT, and institutional research) moved further than those that siloed AI within a single department. Successful adoption demands coordination across the institution, not just technical deployment.

Myth 4: AI Will Solve Data Problems

This misconception proved particularly costly for institutions that rushed to implement AI tools before addressing underlying data challenges. AI can only provide insights as strong as the data it’s built on. Institutions with fragmented, siloed, or incomplete data found that AI tools amplified existing problems rather than solving them.

Several leaders described AI as “duct tape” being applied to systems with fundamental design flaws. Before AI can meaningfully enhance decision-making, institutions must first invest in data integrity and governance.

Myth 5: Faculty, Staff, and Students Already Know How to Use AI

Perhaps the most pervasive assumption we encountered was that widespread familiarity with tools like ChatGPT translates to AI literacy. It does not.

Many students use generative AI casually but lack training in responsible use, bias detection, and integration into academic or professional work. Similarly, faculty and staff often rely on trial-and-error experimentation without structured support. Our research found that without institutional investment in training, adoption risks being shallow, inconsistent, or misaligned with institutional goals.

Critically, students cannot fully develop AI literacy unless their instructors and staff know how to model it. Faculty and staff literacy isn’t separate from student readiness; it’s the foundation for it.

The Bigger Picture

These myths matter because they shape resource allocation, strategic planning, and institutional culture. Our research found that only 17% of institutions interviewed exhibited indicators of strategic AI integration, including executive leadership engagement, comprehensive policies, and dedicated budgets. The remaining 82% were still in experimental or scaling phases.

Resource levels played a significant role: the four best-resourced institutions in our study all reached transformational AI adoption, while none of the resource-constrained institutions achieved that status. This widening gap underscores the equity implications of AI adoption and the urgency of grounding conversations in evidence rather than hype.

What Institutions Can Do

Moving beyond these myths requires intentional action:

• First, use our AI Adoption Rubric or Survey Suite to assess your current readiness across all five adoption readiness categories. Understanding where your institution stands is essential before making investment decisions.
• Build cross-functional teams that treat AI as an institutional strategy, not a technology project.
• Prioritize structured training for faculty, staff, and students rather than assuming literacy will emerge organically.
• Invest in data governance before scaling AI tools.
• Develop realistic cost planning that accounts for training, infrastructure, and change management, not just licenses.
• Connect with peer institutions to share knowledge, reduce duplicated efforts, and build collective capacity.

Learn More

Our full research report, Adopting AI in Higher Education: Patterns, Challenges, and Emerging Practices, offers a deeper look at adoption patterns across institution types, the barriers shaping implementation, and emerging practices from institutions making progress. We’ve also released a series of companion resources, including tools for assessing institutional readiness, designing training strategies, and planning for real and hidden costs, available at t3advisory.com/ai-for-institutional-transformation.

As the digital learning community continues navigating AI’s implications for teaching, learning, and operations, we hope these findings help cut through the noise and ground conversations in what’s actually happening across the sector.

This research was conducted by T3 Advisory in partnership with Complete College America. Author: Audrey Ellis, Founder and Principal Consultant, T3 Advisory

Author: Audrey Ellis, Founder and Principal Consultant, T3 Advisory