There is significant discussion about the opportunities and issues associated with the use of artificial intelligence in higher education. However, issues, such as academic integrity and privacy, continue to dominate the conversation. This has limited how well instructors and higher education institutions can identify the conditions necessary to support AI use, to benefit all students. The present synthesis of qualitative evidence has explored the available evidence from 2019-2024, to consider the opportunities and conditions of AI use in higher education learning. The result is five synthesized findings addressing this issue at both institutional and learning design levels and considering subcategories ranging from access, to interactions to future learning. Based on the synthesized findings as the proposed AIMED model, addressing the conditions necessary to create opportunities of AI-tool use in higher education for all students. Implications and future research are explored.
|
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- The AIMED model: Creating opportunities for AI in Higher Education - May 20, 2025
- Biased AI Writing Assistants Shift Users’ Attitudes on Societal Issues - May 20, 2025
- How Adding Metacognitive Requirements in Support of AI Feedback in Practice Exams Transforms Student Learning Behaviors - May 20, 2025