AI Adoption in Higher Ed

Today, I would like to share a recent AI SoTL article entitled “Assessing the adoption of artificial intelligence in higher education: A case study” by Hung, Son, My, Anh, Anh and Minh (2026) (https://doi.org/10.3991/ijet.v21i01.59557 ).

Using a mixed methods approach, the researchers surveyed 156 lecturers and conducted interviews with 20 faculty members to evaluate the current level of AI adoption, barriers to integration, and institutional strategies needed to support meaningful implementation. The study is grounded in the Technology Acceptance Model (TAM), which examines how perceived usefulness and ease of use influence technology adoption.

Findings

• 67.9% of faculty had experimented with AI tools, often driven by the rapid rise of AI platforms.

• 12.8% reported using AI tools daily in teaching, indicating that adoption remains mostly exploratory rather than pedagogically integrated.

• Institutional training was limited, with 15.4% receiving formal AI training from their university.

• Major barriers included lack of infrastructure, concerns about academic integrity, and uncertainty about data quality and ethical use of AI.

• Despite these barriers, 89% expressed interest in PD focused on AI in teaching.

• The study recommends a phased institutional strategy for AI integration, including faculty training, infrastructure development, and clear policy guidance.

The research offers several practical implications for instructors and institutions seeking to integrate AI effectively:

1. Establish Structured Faculty Development Programs. Institutions should offer workshops and microcredentials focused on AI literacy, prompt design, and ethical AI use in teaching.

2. Design AI-Supported Learning Activities. Faculty can use AI tools to support brainstorming, formative feedback, and iterative learning cycles while maintaining human evaluation of higher-order thinking.

3. Develop Institutional AI Policies and Guidelines. Clear expectations around academic integrity, transparency, and responsible AI use help reduce uncertainty among faculty and students.

4. Integrate AI into Curriculum Design. Assignments can ask students to analyze AI outputs, critique generated responses, and improve them, thereby strengthening critical thinking and metacognitive skills.

5. Support Infrastructure and Communities of Practice. Faculty learning communities can share AI-supported teaching practices, accelerating adoption through collaborative experimentation.

Reference

Hung, N. N., Son, P. N., My, N. T., Anh, N. T. M., Anh, T. V., & Minh, N. T. (2026). Assessing the adoption of artificial intelligence in higher education: A case study of Hanoi Metropolitan University. International Journal of Emerging Technologies in Learning, 21(1), 73–92.