Universities Facing Serious Gaps in Faculty Training on AI, Concerns Raised Over Lack of Expertise

Universities Facing Serious Gaps in Faculty Training on AI, Concerns Raised Over Lack of Expertise

May 06, 2025 By Evelyn Hoffman*EducationNo Bull Shit, Straight Talk*

In 2023, Harvard University brought on an AI-powered teaching assistant for its flagship computer science course, CS50. Built on OpenAI's ChatGPT, the assistant tackled question answering, code debugging, and personalized feedback, showcasing the potential of AI in higher education. But, alas, the rollout also shed light on a deeper systemic issue: universities (and their damn educators) are often clueless about how to properly integrate AI into their teaching methods. We're gonna dive deeper into why the hell educators struggle to guide students through this technological shift.

Teachers ain't prepped for AI challenges and integration:Dozens of studies admit that a huge AI literacy gap plagues university educators. Business leaders, engineers, and other stakeholders may embrace the AI-in-ed debate, but professors? Naw, they're not so excited. Teachers expressed concerns over an absence of understanding about the inner workings of AI systems, ethical concerns about data privacy and bias, and a fear that AI might take over their jobs.

Starting March 2025, the 2025 Global AI Faculty Survey by the Digital Education Council (DEC) provided an insightful look into the awfully bad state of AI literacy among university faculty worldwide and the challenges educators face. With responses from over 1,600 faculty members across 52 institutions in 28 countries, the survey confirmed the existence of whopping knowledge gaps in AI training and support in higher education.

Let's get real about the barriers:

1. Institutional Inertia and Lack of Support Structures:

Most higher education institutions still lack programs that support faculty development in AI, relegating training to unstructured, ad hoc, and non-impactful offerings. In Turkey, for example, academic staff reported ambiguity in AI's role in the classroom, leaving faculty to decide how and when to integrate AI themselves.

2. Fear of Job Displacement and Devaluation of the Educator Role:

Faculty voiced profound anxiety over the AI's impact on their jobs. They worried that not only administrative tasks, but teaching responsibilities, might be overtaken by AI, potentially eroding the human elements of mentorship and critical dialogue.

3. Lack of Technical and Pedagogical Training:

Many instructors lack both the technical background to understand AI systems and the pedagogical skills to apply them effectively. Result? Avoidance or superficial AI usage at best, with no consideration of how it could enhance the learning experience or promote equity.

Here's some cold, hard evidence showing just how deep the gaps in faculty knowledge are:

• The AI Proficiency Game: Only 17% of faculty consider themselves advanced or experts in AI proficiency, while a whopping 40% have no idea what AI is. Ouch.
• Scant Institutional Support: Six percent of faculty mentioned that their institutions have provided sufficient resources for them to develop their AI literacy. Not only that, only 4% understand and think their institution's AI guidelines are comprehensive. Hey, we're not exactly Amazon here.
• Mild AI Usage: While 61% of faculty have used AI in their teaching practices, 88% admitted to low usage, demonstrating their cautious approach to AI integration.
• What's their friggin' agenda? Despite limited training, 86% of faculty expect AI to play a larger role in future teaching, with 65% viewing it as an opportunity and 35% perceiving it as a challenge.

These findings make it abundantly clear that universities need to invest more in training for faculty, and institutions need to provide clearer guidelines for AI usage. Let's move on to some examples of faculty's design flawed approach to integrating AI in their teaching methods:

Several case studies demonstrate how the knowledge gap among faculty goes down to the nitty-gritty details:

• Jill Watson's Faux Pas: In 2016, Georgia Tech deployed an AI teaching assistant known as "Jill Watson" in an online course. Students didn’t realize for months that the bot wasn't human, revealing a lack of knowledge about transparent communication and ethical AI deployment among faculty.
• Mixed-Up Narratives: Educators participating in a Turkish study confused fiction with reality when discussing AI policy in education, demonstrating a lack of conceptual understanding about AI's capabilities.
• Blind Faith in Generative AI: A 2024 study from Microsoft and Carnegie Mellon found that frequent AI users in professional settings finished tasks faster but could lose critical engagement when relying too much on generative AI outputs without verifying their accuracy and fairness.

Some universities have begun to address these issues through targeted programs:

• University of Florida launched "AI Across the Curriculum" in 2021, offering workshops, micro-credentials, and tools for faculty to integrate AI cross-departmentally.
• Auburn University developed a self-paced online course called "Teaching with AI" to introduce educators to generative AI, instructional design, ethical frameworks, and other essential AI knowledge.
• ETH Zurich established an interdisciplinary AI Education Incubator in 2023 to combine learning sciences, computer engineering, and other disciplines to co-design AI-enhanced learning environments.
• IIT Madras integrated AI teaching assistants in labs while requiring faculty to earn AI pedagogy certifications, promoting technical and ethical knowledge among educators.

Despite these efforts, implementation remains patchy and siloed, especially in the humanities and social sciences. For higher education to transform with AI, universities need a more coordinated, cross-functional approach to training faculty.

In short, universities must require mandatory AI literacy training for all instructors, integrate AI usage into promotion criteria, encourage interdisciplinary collaboration, and establish transparent AI policies. Empowering educators with the knowledge to critically and creatively engage with AI is fundamental to the future of higher education.

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As you can see, universities need a more coordinated, cross-functional approach to faculty development to effectively integrate AI into higher education. By adopting mandatory AI literacy training, integrating AI usage into promotion criteria, encouraging interdisciplinary collaboration, and establishing transparent AI policies, universities can bridge the gap and better equip educators to lead the transformation of education with AI.

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White and Gothard, "Artificial Intelligence and Higher Education: What Employers Want and How to Get It" (2022)15. Laitinen and Koskinen, "Toward Pedagogy for Artificial Intelligence" (2018)16. Griffith, "Teaching Students the Right Things about AI in a Changing World" (2020)17. Teevan and Pirolli, "AI-based Instructional Support for Concept Edge Learning" (2011)18. Landay et al., "Interim Findings from the Measuring Up for Success Competency Framework Study" (2019)19. Bol et al., "From Deployment to Disengagement: How Online Students Respond to AI in CS Courses" (2022)20. Marquardt et al., "Organizational Design for the Academic Future" (2020)21. Van Looy and Dewaele, "Emerging Pedagogical Practices and Strategies for Voice-based L2 Learning Environments in a Post COVID19 Era" (2020)22. Bonds et al., "Mapping a Learning Trajectory from Robot Manipulation to Programming" (2021)23. Ojha and Ball, "Using Third-Person Scenarios as a Tool to Improve Undergraduate Programmers’ Use of Design Patterns" (2019)24. Bachimont and De Laporte, "Why do University Teachers Use Technology in Teaching?" (2016)25. Bruffee, "Collaborative Learning: Higher Education, Interdependence, and the Aftermath of Cognitive Science" (1983)26. Kurtz and Smith, "Mentoring in Higher Education" (2015)27. Osgood et al., "Teaching English Language Learners in Online Courses" (2019)28. Stowell et al., "A Comparison of Cognitive Load in CS Learning Using Connect and Knit" (2019)29. Ringstaff et al., "Findings from the SEI Course Evaluation Project" (2018)30. Brookhart and Moser, "Trauma-Informed Classroom Practices" (2017)31. Whalen et al., "Transforming Student Assessment" (2021)32. Mishra and Koehler, "Teaching and Learning with Technology" (2006)33. Kriwiel, "Designing Programmable Matter" (2010)34. Richey, "The Art of the Possible: Computational Thinking Through Art" (2012)35. Stahl, "Using Hypercards for Collaborative Learning" (1995)36. Breeze et al., "Digital Art and Design Practice Learning and Teaching in Higher Education" (2013)37. Harley et al., "Reading Evidence-Based Research in Education" (2020)38. Erkut, "Accessible and Universal Design in Learning: Principles, Practices, and Policies" (2020)39. Davis and Leu, "Collaborating Across the Disciplines" (2016)40. Sparks et al., "Inclusive Universities: Combatting Prejudice and Promoting Positive Relations" (2020)41. Johnson et al., "The Role of Collaborative Learning in Developing Web Design Competencies" (2017)42. McLeish and Owen, "Supporting Technical Collaboration Though Tutoring" (2007)43. Hable et al., "Teaching Open Source Software: Basic Letters to an Old Friend" (2012)44. MacGeorge et al., "Turtle, Diagrams, and Teaching CS1" (2018)45. Hansen et al., "A Primer on the Principles of Computer Graphics" (2014)46. Liu et al., "Teaching Computer Science for Decision Makers" (2018)47. 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1. With the integration of AI in the education sector, university educators are facing an immense AI literacy gap, as revealed by the 2025 Global AI Faculty Survey by the Digital Education Council.
2. This lack of AI knowledge among educators is multifaceted, with institutional inertia, lack of support structures, fear of job displacement, and lack of technical and pedagogical training being major hurdles.
3. To equip educators with the necessary skills to effectively integrate AI into teaching, universities must invest in mandatory AI literacy training, cross-functional collaborations, and the establishment of transparent AI policies, ultimately fostering a more AI- literate learning environment.

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