Are Students and Professors Really That Different? A New Study Challenges Assumptions About AI in Higher Ed

Redacción HC
15/03/2025

As generative AI tools like ChatGPT become more common in universities, one question keeps emerging: how do students and faculty actually feel about them?

A recent study published in Innovative Higher Education (January 2025) and conducted by a team from Virginia Tech sheds light on this issue, offering surprising insights into how both groups perceive AI tools. The researchers analyzed responses from nearly 1,000 students and 76 faculty members to understand their attitudes, usage habits, ethical concerns, and disciplinary differences. The results reveal unexpected common ground, challenge conventional assumptions, and offer practical recommendations for institutional policy in an AI-enhanced academic world.

What Was the Study Really Asking?

At its core, this research explored how both students and faculty perceive generative AI in university settings. The researchers were particularly interested in five key dimensions:

1. Ease of use
2. Enjoyment (hedonic motivation)
3. Habits and frequency of use
4. Ethical concerns
5. Perceived impact on learning and teaching

One of the main goals was to move beyond speculation and anecdote, providing data-driven evidence on how these tools are viewed in real academic life. The central research question: How do perceptions of generative AI vary between university students and faculty, and how are these shaped by demographic and disciplinary contexts?

A Rigorous Survey Approach: How the Study Was Conducted

This was a cross-sectional observational study carried out during Fall 2023 at a large public university in the southeastern United States. Participants included:

• 982 students
• 76 faculty members

The team designed a detailed survey based on the UTAUT model (Unified Theory of Acceptance and Use of Technology), a widely used framework for understanding tech adoption. It measured constructs like performance expectancy, effort expectancy, hedonic motivation, and intention to use. Questions were administered using a Likert scale and included prompts about ethical views and actual usage patterns.

Statistical tools such as confirmatory factor analysis (CFA), Welch’s t-tests, ANOVA with Tukey post-hoc comparisons, and Wilcoxon tests were used to analyze the data in R.

Limitations included a relatively small faculty sample and a focus on a single institution, which may restrict the generalizability of results. Still, the findings are highly relevant for other universities looking to integrate AI more intentionally.

What They Found: Surprises, Confirmations, and Contradictions

The data yielded several key findings:

Similar Intentions, Different Experiences

• No significant differences were found in performance expectations or overall intent to use AI between students and faculty.
• But students reported significantly higher ease of use and greater enjoyment when using AI (p < 0.01 for both measures).

This challenges the often-assumed narrative that students are enthusiastic adopters while faculty are resistant. While both groups recognize AI's utility, students simply find it more intuitive and engaging.

“The assumption that professors lag behind students in tech adoption isn’t always true—at least not in perceived usefulness,” the authors note.

Most People Aren’t Using AI That Much

• Roughly 70% of both groups used generative AI tools less than once per week during the semester.
• Only 6% of students and 9% of faculty used them daily.

This suggests that the AI revolution in education is still in its early stages. There’s widespread interest, but not yet widespread integration.

Disciplinary and Demographic Differences Matter

• STEM students and male respondents had significantly more favorable attitudes toward generative AI than their counterparts in non-STEM fields and female participants (p < 0.001).

This suggests that discipline and gender intersect strongly in shaping how people view and adopt these tools.

Such disparities could amplify existing inequalities if institutions fail to offer inclusive and targeted support.

Why It Matters: Implications for Policy and Practice

The study isn’t just a snapshot—it’s a call to action. Its findings highlight the need for thoughtful, evidence-based strategies to guide AI integration in academia.

Policy Recommendations

1. Develop clear usage guidelines that promote transparency and uphold academic integrity.
2. Design assignments that prioritize critical thinking, making mere AI replication insufficient.
3. Offer targeted support for faculty and students in non-STEM fields, where adoption may lag.
4. Conduct longitudinal and multi-institutional research to track evolving trends.
5. Avoid one-size-fits-all policies—design flexible frameworks that adapt to disciplinary needs.

Educational and Social Impact

The relatively low current usage of AI tools suggests untapped potential, but also risk: without proper training and policy, usage may become uneven and reinforce disparities in access to learning resources.

“The real challenge is not whether AI is good or bad, but whether institutions can create equitable conditions for its effective and ethical use,” the authors argue.

Conclusion: A Nuanced Reality Demands Nuanced Solutions

This study paints a more complex picture than headlines might suggest. Students and faculty are not as divided as we think—but the way they interact with AI tools is shaped by experience, confidence, and academic culture.

If universities want to harness the full potential of generative AI, they must go beyond assumptions and implement data-informed, inclusive, and adaptive policies. This research offers a foundational map for that journey.

Call to action: If you’re a university policymaker, educator, or student advocate, use this study as a starting point. Listen to your community’s perceptions and needs—and let that guide your approach to AI in education.

Topics of interest

Academia Technology