AI and the Future of Learning: Expert Panel Report
Open accessAbstract
This report is based on the discussion that emerged from a convening of a panel of 22 experts in artificial intelligence (AI) and in learning. It introduces three layers that can frame the meaning of AI for educators. First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths. Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion that the most impactful uses of AI in education have not yet been invented. The report enumerates important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning. Through discussions among experts about these layers, we observed new design concepts for using AI in learning. The panel also made seven recommendations for future research priorities.
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Roschelle et al. observe that AI improves accessibility, flexibility, and active student engagement, especially in mixed and remote learning contexts.
“Similarly, Roschelle et al. [29] observe that AI improves accessibility, flexibility, and active student engagement, especially in mixed and remote learning contexts.”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [29]
AI offers novel opportunities but also introduces profound pedagogical challenges for universities, necessitating the adoption of innovative approaches to instruction and curriculum design.
“As noted by Roschelle et al. [29], AI offers novel opportunities but also introduces profound pedagogical challenges for universities, necessitating the adoption of innovative approaches to instruction and curriculum design (Figure 2).”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [29]
In humanities and social sciences, AI use is more reflective and creative, serving as a dialogic partner or tool for argumentation and critical analysis.
“In the STEM fields, for example, AI is typically used to solve structured problems and automate assessment, while in the humanities and social sciences, its use is more reflective and creative-serving as a dialogic partner or a tool for enhancing argumentation and critical analysis [29,58].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [29]
AI integration varies significantly across disciplines.
“Moreover, AI integration varies significantly across disciplines.”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [29]
As Roschelle et al. note, such tools support experiential learning and enhance student engagement, particularly in blended or distance learning environments.
“As Roschelle et al. [29] note, such tools support experiential learning and enhance student engagement, particularly in blended or distance learning environments.”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [29]
In STEM fields, AI is typically used to solve structured problems and automate assessment.
“In the STEM fields, for example, AI is typically used to solve structured problems and automate assessment, while in the humanities and social sciences, its use is more reflective and creative-serving as a dialogic partner or a tool for enhancing argumentation and critical analysis [29,58].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [29]
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- Expert Panelists: Dr. Russell Almond, Florida State University; Dr. Ryan Baker, University of Pennsylvania; Dr. Avron Barr, IEEE; Dr. Gautam Biswas, Vanderbilt University; Dr. Justine Cassell, Carnegie Mellon University; John Cherniavsky, National Science Foundation;
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Heyman, E. (2021) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-8032-5.ch015
Hadjiski, M., & Kaltenborn, R. (2021) · International Conference Automatics and Informatics (ICAI) · doi:10.1109/icai52893.2021.9639626
AI Transformation for Learning in Organizations
Bidarra, J., & Neves, C. (2024) · Advances in human resources management and organizational development book series · doi:10.4018/979-8-3693-0556-0.ch004
Network Based Methodology for Characterizing Interdisciplinary Expertise in Emerging Research
Mallavarapu, A., Walker, E., Kelley, C., Gardner, S. L., Roschelle, J., & Uzzo, S. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-53499-7_10
Self-citationAtieku-Boateng, H., Osei, R. D., Aryee, V., Essien, L., & Atieku-Boateng, B. (2024) · Research Square · doi:10.21203/rs.3.rs-4550887/v1 · Cited by 1 · see citing works
Tough Times in Education: The Covid-19 CrisisPreprint
Momani, A. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5023170
Reshaping Education Through AI: A Progressive Model for Developing Economies
Avanesh, N. M., & Zachariah, M. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-72410-7_11
Leveraging AI in Educational Assessment
Sundari, V. M., Vaishnavi, V., & Kumar, G. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6170-2.ch005
Comparing Inclusive Education Practices Implemented in Schools According to Stages of Education
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Özdere, M. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub590.c2455
Güncel Eğitim Yönetimi Uygulamaları
Erdemir, B., Özdere, M., Karaman, H. S., Maya, İ., Yılmaz, Z., Kılçık, F., Serin, H., Dörterler, S. Ö., Uslu, S., Gezmen, R., et al. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub590
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Development for AI Adoption Inclusive and Equitable AI in Diverse Learning Environments
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- Authors
- Roschelle, J., Lester, J., & Fusco, J.
- Publisher
- Digital Promise
- Published
- 2020-11
- OpenAlex
- W3133271749
- Cited by
- 76
- References
- 46
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 27 · 2023: 5 · 2022: 10 · 2021: 7
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, future of learning, computational intelligence, AI capabilities, research priorities, expert panel
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