Building on the previous post about an innovative, theory-based approach that uses fast, interactive sessions to enhance critical thinking and collaboration (linked above), it’s worth highlighting a recent paper by researchers from Vienna University published in the journal Project Leadership and Society.
The study identifies three key hurdles higher education faces in closing the AI skills gap. First, student cohorts are wildly varied—attitudes and fluency differ by gender, discipline, age, socio-economic status and location—so one-size-fits-all frameworks fail. Second, many faculty lack clear policies, resources and AI experience, so they hesitate to embed AI tools. Third, formal curricula and validation processes move too slowly to match rapid AI advances.
To take action now, simple, teacher-led strategies are key. Encouraging students to share their Generative AI knowledge through peer learning helps build confidence. Working together, teachers and students can create AI lessons and ethics talks that make the most of students’ skills. Quick, flexible activities like short workshops, easy assignments, self-paced modules, and hackathons provide focused AI experience without much hassle. These practical steps won’t fix everything overnight but can make a real difference now and pave the way for bigger AI education changes. https://www.sciencedirect.com/science/article/pii/S2666721525000080#sec3
PS - This context also calls for a reminder of the previous post "The Twin Forces Leaving Many Universities Struggling to Remain Relevant—or Even Facing Financial Ruin" https://19-pacheco-torgal-19.blogspot.com/2025/01/the-economistthe-twin-forces-leaving.html
Update on May 21 - A paper published today in the journal Teaching and Teacher Education found that pre-service teachers’ AI acceptance divides into four TAM-based profiles—High Acceptors (19.2 %), Cautious Supporters (54.3 %), Skeptics (16.6 %), and Pragmatic Adopters (9.9 %)—all sharing high privacy concerns but differing in perceived compatibility, transparency, complexity, and digital competence. The authors argue for tailored support—peer mentoring, empirical demonstrations, simplified interfaces, and institutional incentives—to optimize AI integration in teacher education. https://www.sciencedirect.com/science/article/pii/S0742051X25001635#sec6
