Instructor Use of AI

This week I would like to share a bit of a tangent to AI in this study that connects AI with instructors' ability to develop flexible thinking. The AI SoTL article is entitled, “Teachers’ AI Use and Flexible Thinking Skills: Evidence from a Predictive Correlational Study”by Dere and Doğan (2025).

The authors examined the relationship between teachers’ use of AI and their flexible thinking skills within educational contexts. Conducted across public and private schools in Türkiye, the research involved 195 teachers and sought to identify whether greater engagement with AI predicts higher levels of cognitive flexibility.

The researchers employed a predictive correlational design, using three validated instruments:

1. Teacher Perception Scale on the Use of AI in Ed (Üzüm et al., 2024), assessing teaching, learning, and ethical dimensions of AI perception.

2. Flexible Thinking in Learning Scale (Barak & Levenberg, 2016; adapted by Aktaş et al., 2024), measuring openness, adaptability, and acceptance of learning technologies.

3. A demographic questionnaire capturing age, gender, subject, institution type, and AI training background.

Participants’ average scores revealed generally positive attitudes toward AI use and high cognitive flexibility. Reliability analyses confirmed strong internal consistency (Cronbach’s α = .78–.95). Statistical analyses including t-tests, ANOVA, correlation, and multiple regression were conducted with SPSS, adopting a significance level of p < .05.

Findings

1. AI Training Matters – Teachers who had taken AI-related PD exhibited significantly higher flexible thinking skills and greater openness to learning technologies. These teachers demonstrated more innovative approaches and adaptability to new instructional contexts (p < .05; d ≈ 0.6–0.7).

2. Experience Influences Adaptability – Age positively correlated with openness to learning and adaptation to new educational settings, suggesting that professional experience enhances flexibility. Teachers over 36 scored notably higher in these areas than their younger counterparts.

3. Limited Demographic Differences – Gender, teaching subject, and institution type showed no significant differences in either AI use or flexible thinking. This implies that attitudes toward AI are broadly shared across demographic lines.

4. Positive but Modest Correlation – The correlation between total AI use and flexible thinking was statistically significant yet modest (r = .23, p < .05). Regression analyses showed that AI-related learning perception was the only significant predictor of flexible thinking (β = .26, p = .006), explaining 7% of the variance in flexibility scores (R² = .07).

5. Ethical Awareness and Critical Reflection – Teachers who had formal AI education reported lower ethical perception scores, suggesting greater critical awareness of AI’s potential biases and risks, a finding consistent with emerging research on AI ethics (Meylani, 2024).

The authors argue that AI literacy enhances teachers’ reflective judgment, supporting adaptability and creative problem-solving. Their findings align with Chiu’s (2024) model of AI literacy as a synthesis of technical, ethical, and metacognitive competencies and with Luckin’s (2018) view that AI exposure cultivates pedagogical agility. Interestingly, while AI use contributes to flexible thinking, its modest predictive power suggests that cognitive flexibility is multidimensional not only by technology but also by contextual and experiential factors such as institutional culture, teacher motivation, and professional learning networks (Spiro & Jehng, 1990). Teachers with AI experience were more comfortable experimenting with novel tools and rethinking instructional design.

Practical Recommendations

• Integrate AI literacy into teacher training frameworks, emphasizing critical and ethical dimensions.

• Provide sustained PD to ensure equitable access to AI-enhanced pedagogies.

• Encourage reflective, process-based approaches in teacher ed, where instructors document and analyze their AI use.

• Institutionalize mentorship structures, pairing experienced educators with novice teachers to model flexible, technology-enhanced thinking.

I would like to share another recent qualitative study published in the Journal of Academic Ethics by Mulaudzi, et al. (2025) entitled, "Lecturer’s perspective on the role of AI in personalized learning: Benefits, challenges, and ethical considerations in higher education" (https://lnkd.in/e2N-HXeg ). This research offers one of the clearest snapshots to date of how instructors are navigating the promises and pitfalls of AI in personalized learning.

Researchers gathered insights from faculty across nine university faculties to better understand how they are adopting and resisting AI tools. Grounded in the Technology Acceptance Model, the study reveals three dominant themes. First, lecturers increasingly acknowledge AI’s pedagogical value: accelerating feedback, organizing information, scaffolding writing, and supporting differentiated learning pathways. Many describe AI as a “win-win,” offering students tailored support while freeing lecturers to focus on deeper instructional interactions.

Second, the findings illuminate persistent concerns. Faculty fear over-reliance on AI will erode critical thinking, writing proficiency, and academic integrity. Others worry that biased datasets will marginalize local knowledge and reproduce inequities, particularly in multilingual and Global South contexts.

Third, the study highlights emerging ethical strategies. Lecturers are revising assessments to include in-class writing, oral examinations, and locally contextualized tasks that AI cannot easily replicate. Many now require students to disclose how and where AI was used, while others pair AI-use policies with deliberate instruction on its limitations, biases, and proper citation.

Overall, the article argues for a balanced institutional approach, PD, digital-literacy training for students, clear policies for acceptable AI use, and ongoing reflection on the human elements of learning that cannot and should not be automated.

References

Barak, M., & Levenberg, A. (2016). A model of flexible thinking in contemporary education. Thinking Skills and Creativity, 22, 74–85.

Chiu, T. K. F. (2024). What are AI literacy and competency? A conceptual study. Computers and Education: Artificial Intelligence, 5, 100190. 

Dere, Z., & Doğan, N. D. (2025). Teachers’ AI use and flexible thinking skills: Evidence from a predictive correlational study. Journal of Pedagogical Research, 9(4), 259–280. 

Feijóo, C., et al. (2021). AI and education: Guidance for policymakers. UNESCO Publishing.

Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.

Meylani, R. (2024). Ethical challenges of AI integration in classrooms: A teacher perspective. Computers and Education: Artificial Intelligence, 5, 100194.

Mulaudzi, L. V., & Hamilton, J. (2025). Lecturer’s perspective on the role of AI in personalized learning: Benefits, challenges, and ethical considerations in higher education. Journal of Academic Ethics, 23, 1571–1591.

Spiro, R. J., & Jehng, J. C. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multidimensional traversal of complex subject matter. In D. Nix & R. Spiro (Eds.), Cognition, education, and multimedia (pp. 163–205). Erlbaum.