Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation
Generative AI presents opportunities and challenges for higher education stakeholders. While most campuses are encouraging the use of generative AI, frameworks for responsible integration and evidence-based implementation are still emerging. This Curriculum, Instruction, and Pedagogy article offers...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Education |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feduc.2025.1604934/full |
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| author | Julie Schell Julie Schell Julie Schell Julie Schell Kasey Ford Arthur B. Markman |
| author_facet | Julie Schell Julie Schell Julie Schell Julie Schell Kasey Ford Arthur B. Markman |
| author_sort | Julie Schell |
| collection | DOAJ |
| description | Generative AI presents opportunities and challenges for higher education stakeholders. While most campuses are encouraging the use of generative AI, frameworks for responsible integration and evidence-based implementation are still emerging. This Curriculum, Instruction, and Pedagogy article offers a use case of UT Austin’s approach to this dilemma through an innovative generative AI teaching and learning chatbot platform called UT Sage. Based on the demonstrated benefits of chatbot technologies in education, we developed UT Sage as a generative AI platform that is both student- and faculty-facing. The platform has two distinct features, one a tutorbot interface for students and the other, an instructional design agent or builder bot designed to coach faculty to create custom tutors using the science of learning. We believe UT Sage offers a first-of-its-kind generative AI tool that supports responsible use and drives active, student-centered learning and evidence-based instructional design at scale. Our findings include an overview of early lessons learned and future implications derived from the development and pilot testing of a campus-wide tutorbot platform at a major research university. We provide a comprehensive report on a single pedagogical innovation rather than an empirical study on generative AI. Our findings are limited by the constraints of autoethnographic approaches (all authors were involved in the project) and user-testing research. The practical implications of this work include two frameworks, derived from autoethnographic analysis, that we used to guide the responsible and pedagogically efficacious implementation of generative AI tutorbots in higher education. |
| format | Article |
| id | doaj-art-eb8475cda2ee471f88611d085ab7060b |
| institution | DOAJ |
| issn | 2504-284X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Education |
| spelling | doaj-art-eb8475cda2ee471f88611d085ab7060b2025-08-20T02:50:14ZengFrontiers Media S.A.Frontiers in Education2504-284X2025-07-011010.3389/feduc.2025.16049341604934Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementationJulie Schell0Julie Schell1Julie Schell2Julie Schell3Kasey Ford4Arthur B. Markman5Department of Design, College of Fine Arts, The University of Texas at Austin, Austin, TX, United StatesDepartment of Educational Leadership and Policy, College of Education (Courtesy appointment), The University of Texas at Austin, Austin, TX, United StatesOffice of Academic Technology, Office of Academic Affairs, The University of Texas at Austin, Austin, TX, United StatesDepartment of Applied Physics, School of Engineering and Applied Sciences (Courtesy appointment), Harvard University, Cambridge, MA, United StatesOffice of Academic Technology, Office of Academic Affairs, The University of Texas at Austin, Austin, TX, United StatesDepartment of Psychology, College of Liberal Arts, The University of Texas at Austin, Austin, TX, United StatesGenerative AI presents opportunities and challenges for higher education stakeholders. While most campuses are encouraging the use of generative AI, frameworks for responsible integration and evidence-based implementation are still emerging. This Curriculum, Instruction, and Pedagogy article offers a use case of UT Austin’s approach to this dilemma through an innovative generative AI teaching and learning chatbot platform called UT Sage. Based on the demonstrated benefits of chatbot technologies in education, we developed UT Sage as a generative AI platform that is both student- and faculty-facing. The platform has two distinct features, one a tutorbot interface for students and the other, an instructional design agent or builder bot designed to coach faculty to create custom tutors using the science of learning. We believe UT Sage offers a first-of-its-kind generative AI tool that supports responsible use and drives active, student-centered learning and evidence-based instructional design at scale. Our findings include an overview of early lessons learned and future implications derived from the development and pilot testing of a campus-wide tutorbot platform at a major research university. We provide a comprehensive report on a single pedagogical innovation rather than an empirical study on generative AI. Our findings are limited by the constraints of autoethnographic approaches (all authors were involved in the project) and user-testing research. The practical implications of this work include two frameworks, derived from autoethnographic analysis, that we used to guide the responsible and pedagogically efficacious implementation of generative AI tutorbots in higher education.https://www.frontiersin.org/articles/10.3389/feduc.2025.1604934/fullgenerative AI (GenAI)chatbotsresponsible AIinstructional design (ID)educational technologyhigher education |
| spellingShingle | Julie Schell Julie Schell Julie Schell Julie Schell Kasey Ford Arthur B. Markman Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation Frontiers in Education generative AI (GenAI) chatbots responsible AI instructional design (ID) educational technology higher education |
| title | Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation |
| title_full | Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation |
| title_fullStr | Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation |
| title_full_unstemmed | Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation |
| title_short | Building responsible AI chatbot platforms in higher education: an evidence-based framework from design to implementation |
| title_sort | building responsible ai chatbot platforms in higher education an evidence based framework from design to implementation |
| topic | generative AI (GenAI) chatbots responsible AI instructional design (ID) educational technology higher education |
| url | https://www.frontiersin.org/articles/10.3389/feduc.2025.1604934/full |
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