The following faculty received Generative AI Software Licenses through TLTR in the 2024-2025 or 2025-2026 academic year.
Jeffery Downs
Kim Garza
Risa Ginther
Jeremy Johnson
Daniel Lievens
Cory Lock
Drew Loewe
Nancy Salisbury
Teri L. Varner
Curt Yowell
Melissa Alvarado
Michael Disch
Jessenia Garcia
Selin Guner
Kris Sloan
Katherine Trevino
Katherine Lopez
Yongshin Park
Chen Xu
Gentry Atkinson
Robert Furey
Paul Savala
Bilal Shebaro
These pilots were approved in Fall 2025 for completion in Spring 2026.
The Teaching, Learning, and Technology Roundtable (TLTR) is excited to announce a pilot program for BoodleBox, an enterprise-level, multi-Large Language Model (LLM) tool, designed to support faculty in integrating Generative AI into their teaching and learning practice. We are seeking interested faculty to join us in exploring the curricular and pedagogical potential of this tool, with the potential to propose a limited student pilot in the Spring 2026 semester.
We have secured 200 faculty licenses for the Spring 2026 semester. This pilot was extended through Summer 2026.
More information is here: BoodleBox Pilot Spring 2026
The Teaching, Learning, and Technology Roundtable (TLTR) is excited to announce a pilot program for BoodleBox, an enterprise-level, multi-Large Language Model (LLM) tool, designed to support faculty in integrating Generative AI into their teaching and learning practice. We are seeking interested faculty with demonstrated AI expertise to join us in exploring the curricular and pedagogical potential of this tool, by running a limited student pilot in the Spring 2026 semester.
Your participation is crucial to helping the university make informed decisions about future AI tools. The student pilots will:
Support Curricular and Pedagogical Consideration: Help faculty explore the impact of AI on current assignments and course design.
Support Teaching AI to Students: Evaluate how Boodle Box can be used to help students learn to use AI responsibly and critically.
Evaluate Enterprise Potential: Gather essential data to determine the potential of Boodle Box as a campus-wide tool.
Provide Equitable Tool Access: Provide students with access to the same tool with access to paid LLM models to ensure equitable learning opportunities
Streamline Instruction: Offer students streamlined instruction based on the same, known generative AI platform.
More information is here: Student Boodle Box License
Read the final report here: Final Spring 2026 BoodleBox Pilot Evaluation Report
During the Spring 2026 semester, the TLTR sponsored a pilot of BoodleBox funded with $10,000 from the TLTR budget, though less than $5000 was ultimately used since BoodleBox bases costs on actual usage.
The initiative evaluated the platform's potential as a campus-wide tool across five core areas:
supporting curricular and pedagogical consideration,
developing faculty AI competencies,
supporting teaching AI to students,
evaluating enterprise potential, and
providing equitable access to paid Large Language Models (LLMs).
The pilot yielded a distinct equity paradox. While BoodleBox successfully provided premium AI models to a student cohort where 64% lacked personal paid AI subscriptions, this access did not translate into high organic engagement. Both student and faculty data reveal significant technical friction, including platform lag, interface complexity, and degraded response quality compared to direct LLM access. Consequently, overall engagement remained low and shallow.
Final Recommendation: Based on these mixed findings, this report recommends against campus-wide enterprise procurement. Instead, the TLTR should approve a downscaled, highly controlled classroom research pilot for Fall 2026. Backed by a reduced $5,000 budget to secure 125 student seats, this project will pivot away from software adoption and focus entirely on first-year AI literacy development within Freshman Seminars and introductory 1000/2000-level courses. By using BoodleBox purely as a data-tracking laboratory sandbox, faculty will design and test repeatable assignment strategies while implementing mandatory pre- and post-pilot diagnostic metrics to measure actual growth in student AI literacy.
Instructional Technology and/or TLTR may also want to maintain a pool of licenses as an AI training support for faculty and staff new to AI.