EDU800 – Week 12 – Additional Articles

#1

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27.

Summary
In this systematic review, Zawacki-Richter and colleagues (2019) analyze 146 peer-reviewed studies to map how artificial intelligence is being applied in higher education. They categorize AI applications into five areas: profiling and prediction, intelligent tutoring systems, assessment and evaluation, adaptive systems, and institutional support. Across these domains, the authors find that most AI research and implementations are driven by computer scientists rather than educators, resulting in tools that prioritize automation and data processing over pedagogy. The review highlights that AI tools are often tested in controlled environments rather than authentic classrooms, raising concerns about generalizability. The authors conclude that higher education needs stronger educator involvement to ensure that AI tools are aligned with learning theory, motivation, assessment validity, and actual instructional practice.

Evaluation
As I reviewed this article, I found it particularly compelling because it blends empirical breadth with a clear critique of how AI is being positioned in higher education. The systematic review method provides strong credibility, and the categorization of AI use cases is helpful for understanding current trends. The authors’ argument that educators are underrepresented in AI development resonates strongly with what I see in both academic and IT professional contexts. AI initiatives often emphasize efficiency—automated grading, predictive analytics, chatbots—without considering how these tools affect learner autonomy, cognitive load, or engagement. One limitation is that the article focuses on studies published up to 2018, meaning it predates the explosion of generative AI; however, the foundational concerns about educator involvement and pedagogical alignment remain highly relevant.

Reflection
This article intersects directly with my teaching practice at DeVry, my doctoral research trajectory, and my professional work as an IT project manager. In the classroom, I frequently evaluate new tools—from Engageli analytics to AI-assisted tutoring—and this review reinforces the need to critically assess how AI aligns with instruction rather than assuming it inherently improves learning. It also echoes what I see in my MSP role, where organizations often adopt AI-infused systems without fully understanding their limitations or pedagogical implications. As a DET student, this article strengthens my interest in researching how AI can support—not replace—human-centered teaching practices, especially in online and blended environments. It also underscores the importance of instructor agency and thoughtful design when integrating AI into digital course structures such as Canvas modules, Excel labs, and discussion-based learning. Ultimately, this study reinforces my belief that AI adoption in education must be grounded in sound pedagogy, transparency, and an understanding of how learning actually happens.