Yesterday, the Online Learning Consortium published its June 2026 issue of the Online Learning Journal, featuring a special focus on Higher Education in an AI-Transformed World. The issue includes a collaborative and reflective UMBC case study examining how artificial intelligence can be used to personalize, scale, and encourage student practice and reflection.
In the article, "Asked & Answered: Using AI to Nudge Student Metacognition and Responsibility for Learning," four UMBC faculty and three DoIT Instructional Technology staff analyze four courses that vary widely in discipline, size, and technology. Despite their differences, each course shares a common pedagogical goal: cultivating students' willingness and ability to assess what they truly know, understand and can do through formative practice.
The UMBC co-authors include:
- John Fritz, Associate Vice President for Instructional Technology (DoIT)
- Josh Abrams, Instructional Technology Design Specialist (DoIT)
- Sarah Bass, Associate Teaching Professor, Chemistry & Biochemistry
- Suzanne Braunschweig, Teaching Professor, Geography and Environmental Systems
- Tara Carpenter, Teaching Professor, Chemistry & Biochemistry
- Nancy McAllister, Assistant Teaching Professor, Interdisciplinary Science Program
- Tom Penniston, Coordinator of Learning Analytics (DoIT)
The publication highlights how different environments can and do leverage AI to create a virtual "Holodeck" for reflective practice — providing a personalized ecosystem where students can actively close the gap between what they think they know and what they actually understand.
Featured use cases in the study include:
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CHEM 101 & 102: UMBC’s largest courses (serving over 800 students annually) use AI to provide a "24/7 prof" and formative environment rooted in "spaced practice" to counter traditional exam cramming.
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SCI 100: A lab science course for non-STEM majors (serving 600 students annually) explores using AI to streamline and curate student-crowdsourced study guides.
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UNIV 102: A smaller course supporting students on academic probation uses AI to inform a team-based, extra-credit practice environment for weekly quizzes, assisting at-risk students in building effective study groups.
Ultimately, the article explores how institutions can use emerging AI tools not as passive lecture supplements, but as interactive spaces that nudge students' self-regulated learning through reflective practice.
To learn more about the findings and specific course frameworks, read the full, open-access article in the Online Learning Journal.