AI Practical Examples for Ed

I began sharing just-in-time summaries of teaching research articles (SoTL) on October 15, 2016 (time flies)! During those nine years, I tried not to share research that I have been part of, but this week I would like to offer a paper that came out this week. The article is entitled “Generative AI for Higher Education: Practical Example to Support Faculty in Creating an Engaging Learning Environment” by Al-Shawwa, Hargis, Qi, & Grewell (2025). In this paper, we argue that, much like the personal computer or the internet, AI represents a threshold tech, one that can redefine pedagogical design and faculty practice when adopted with intentionality and ethical foresight.

Drawing on multi-institutional qualitative case studies, this study investigates how faculty across disciplines are preparing to integrate AI into their courses. It provides both a theoretical framework grounded in the Technological Pedagogical and Content Knowledge (TPaCK) model (Mishra & Koehler, 2009) and a series of practical examples for implementation in course design, formative feedback, and learning assessment.

The study centers on a guiding research question, how can AI be integrated into higher education pedagogy to empower instructors in creating accessible, engaging, and ethical learning environments for all students? To answer this, the authors emphasize aligning AI applications with Wiggins and McTighe’s (2011) backward design model beginning with learning outcomes, then assessments, and finally instructional methods.

The integration of AI, they argue, must not merely serve as a technological enhancement but as a pedagogical catalyst strengthening formative feedback loops, enabling adaptive and competency-based assessment, and supporting student reflection. These design principles echo Biggs’ (2002) concept of constructive alignment, where teaching methods, assessments, and learning activities cohere around clear learning goals.

The TPaCK framework remains the conceptual backbone of this research. It articulates how the intersection of technology (TK), pedagogy (PK), and content knowledge (CK) must be intentionally balanced to yield effective instruction. The authors extend this framework through the “affective” dimension (Park & Hargis, 2018), acknowledging that emotions, motivation, and ethical awareness play central roles in shaping how educators and students engage with AI.

By applying TPaCK, the study demonstrates that successful AI integration requires:

1. Technological Knowledge (TK): Understanding the affordances and limits of AI tools.

2. Pedagogical Knowledge (PK): Maintaining learner-centered design and formative feedback practices.

3. Content Knowledge (CK): Ensuring that disciplinary rigor and foundational knowledge remain intact despite automation.

The TPaCK framework thus safeguards against the over-technologization of education reminding faculty that AI should serve pedagogy, not define it (Voogt et al., 2013).

A distinctive methodological contribution of this study is its reflexive use of AI within the research process itself. Using ChatGPT for keyword extraction, thematic clustering, and sentiment analysis, the authors explored AI as both a subject of inquiry and an analytic collaborator. Approximately 15% of the transcripts were manually coded to verify the AI-generated outputs—a methodological triangulation that highlights both the potential and the risks of AI-assisted research (Yuan et al., 2022). 

Across cases, faculty shared a collective philosophy, AI should be embraced as a pedagogical partner, not a policing mechanism. While all participants acknowledged ethical concerns around plagiarism and intellectual property, they emphasized that banning AI outright is counterproductive. Instead, AI can be used to:

• Enhance formative feedback (Lockard & Hargis, 2017);

• Foster critical thinking and reflection (Zimmerman, 1998); and

• Support inclusive and accessible pedagogy by personalizing content delivery (Rodriguez & Koubek, 2019).

The authors connect their findings to constructivist and andragogical learning theories. AI, when embedded within active learning frameworks, can amplify learner autonomy, self-regulation, and motivation key tenets of experiential and adult learning theory (Knowles, 1980).

The authors call for continued empirical research that expands sample sizes, examines longitudinal effects, and refines ethical protocols for AI use. Ultimately, the study affirms that when guided by pedagogical intent and human-centered design, AI can foster engagement, inclusivity, and innovation without compromising academic integrity.

References

Al-Shawwa, R., Hargis, J., Qi, H., & Grewell, C. (2025). Generative AI for higher education: Practical example to support faculty in creating an engaging learning environment. Global and Lokal Distance Education (GLOKALde), 11(2), 1–20.

Biggs, J. (2002). Aligning teaching and assessment to curriculum objectives. Higher Education Academy.

Bransford, J., Brown, A., & Cocking, R. (1999). How people learn: Brain, mind, experience, and school. National Academy Press.

Lockard, E., & Hargis, J. (2017). Andragogical design thinking: A transition to anarchy beyond the classroom. Transformative Dialogues, 10(4), 1-15.

Mishra, P., & Koehler, M. J. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1).

Park, E., & Hargis, J. (2018). New perspective on the TPACK framework: The “A” stands for affective. International Journal for the Scholarship of Teaching and Learning, 12(2).

Voogt, J., Fisser, P., Roblin, N., & Tondeur, J. (2013). Technological pedagogical content knowledge: A review of the literature. Journal of Computer Assisted Learning, 29(2), 109–121.

Yuan, Y., Hargis, J., Lu, H., Lian, J., Huang, X., & Song, Y. (2022).  A qualitative investigation into instructors’ reflections on rapid migration to online teaching. Transformative Dialogues: Teaching and Learning Journal, 14(3), 66-89.