Improving teaching through data-informed practices

We help instructors collect, analyze, and interpret rigorous quantitative and/or qualitative data on learning outcomes to iteratively refine teaching and learning, including:

147 educators using learning data to improve outcomes in their courses and/or academic programs.

142 Early Course Feedback Services gathering anonymous, formative feedback from students for 80 instructors in 119 CMU courses.

Centering inclusive excellence in our programs & services

We help instructors recognize and identify effective, inclusive strategies to incorporate in their teaching practices. At the university, college, and department levels, we designed and facilitated:

10 group events attended by 125 faculty/staff instructors (who filled 143 seats).

9 group events focused on inclusive excellence attended by 632 postdoc, graduate, and undergraduate instructors (who filled 688 seats).

We also supported 15 Provost’s Inclusive Teaching Fellowships implementing projects to enhance course design and/or delivery.

Enhancing teaching innovation & learning with technology

We provide Technology-Enhanced Learning (TEL) tools and services to support educators in creating new or enhanced teaching and learning opportunities. This year we:

Responded to 3,940 instructor requests for technical support with various tools (e.g., Canvas, Gradescope, plagiarism detection, generative AI).

Supported 126 instructors incorporating TEL in remote and in-person modalities.

Sustained and evolved CMU’s TEL ecosystem to meet teaching and learning needs via: 44 third-party TEL tools (including generative AI tools and 5 new tool development efforts).

Consultations served approximately 1 in 5 CMU faculty

In AY 2025-26, we provided consultation services for:

320 faculty/staff educators teaching 327 CMU courses (>10% of CMU courses) on evidence-based teaching strategies, technology-enhanced learning, and/or assessment of student outcomes.

47 educators at CMU satellite campuses:

• 34 CMU-Qatar, 6 CMU-Silicon Valley, 6 CMU-Africa, and 1 at CMU-DC.

39 Eberly Center Fellowship Projects for CMU Instructors of Record: 

• 11 Generative AI Teaching as Research (GAITAR) Fellowships for faculty
• 15 Provost’s Inclusive Teaching Fellowships (PITF) for faculty
• 6 Wimmer Faculty Fellowships for the Development of Teaching
• 7 Active Learning Incubator Faculty Fellowships

• 985 Teaching Assistants and Instructors of Record attended campus-wide Graduate and Undergraduate Student Instructor Orientations, preparing them for their CMU teaching responsibilities across 47 academic units.
• 201 graduate students and postdocs filled 340 seats at our other university-wide seminars and workshops.
• 120 graduate students and postdocs received 203 individual teaching consultations.
• 38 graduate students and postdocs attended the Mentoring Undergraduate Researchers Institute (co-hosted by the Office of Undergraduate Research and Scholar Development).

We supported 321 graduate students and postdocs in our Future Faculty Program, preparing them to teach effectively and inclusively.

• 63 new participants enrolled in the program.
• 18 participants completed program requirements.

Teaching Consultation Fellows (TCFs)

• We trained 6 graduate student and postdoctoral TCFs in advanced, evidence-based teaching methods and consultation techniques.
• TCFs provided 135 teaching consultation services for 88 graduate students and postdocs.

Teaching Consultant Fellows (TCFs)

Year 3 Highlights:

In AY 25-26, our events supported both adaptation and innovation. 154 faculty, staff, postdocs, and graduate students filled 175 seats at university-wide and unit-level programs, including:

• Eberly-Powered Instructor Community: Generative AI
• GAITAR Fellowship Special Interest Group
• Writing Accountability Groups for Teaching as Research Projects
• Seminars
  • Impacts of Online Modules on Students' GenAI Competencies
  • Impacts of Students’ Generative AI Use on Writing Skills
  • Assessing Impacts of GenAI on Student Learning
  • Learning-Centered Teaching in the Age of GenAI
  • Mitigating the Inappropriate Use of GenAI in Classroom
  • Promoting the Productive Use of GenAI in the Classroom
  • Faculty Discussion on CMU Guidelines for AI & Teaching
  • Research on AI & Teaching

We disseminated results from two projects at CMU and beyond.

• GAITAR@Scale 1 designed interactive, online learning modules about generative AI and measured their impact on students’ generative AI competency (knowledge, skills, and self-efficacy).
  SCOPE: 46 instructors, 53 unique courses, and 2,324 students
  RESULTS: Modules significantly increased students’ generative AI knowledge, prompt engineering skills, and self-efficacy above and beyond a control group.
  PUBLICATION: Connell Pensky, A. E., Eckstein, L. E., Melville, M. C., Pottmeyer, L. O., Mineroff, Z., Chawla, A., Brooks, J., Hershock, C., & Lovett, M. C. (2026). Can asynchronous learning modules about generative AI increase students’ knowledge, skills, and self-efficacy for genAI? Computers & Education, 250. https://doi.org/10.1016/j.compedu.2026.105640
• GAITAR@Scale 2 tested the impact of generative AI feedback versus peer feedback on students’ ability to support a claim with evidence, their self-efficacy, and their perceptions of the revision process in first-year writing courses.
  SCOPE: 16 instructors, 28 courses, and 424 students.
  RESULTS: Students receiving multiple rounds of genAI feedback appeared to perform better than students receiving a single round of human peer feedback. Students in the human peer group reported a different and more positive experience than their peers in the genAI group.
  PUBLICATION: Pottmeyer, L. O., Hershock, C., Wetzel, D. Z., & Mineroff, Z. (2026). Impact of using generative AI as a “feedback partner” on students’ writing quality. Journal on Excellence in College Teaching, 37, 74-94. https://celt.miamioh.edu/index.php/JECT/article/view/1305

• GAITAR@Scale 3 investigated data on the impact of student’s practicing with and receiving feedback from generative AI on writing for a specific audience.
• GAITAR@Scale 4 pilot tested preliminary data on the impact of generative AI on students' engineering estimation skills. A second iteration of data collection is currently under development in collaboration with subject matter experts from the College of Engineering.

GAITAR Fellowships included support from Eberly Center consultants to design, implement, and measure the impacts of a teaching innovation using a generative AI tool in a CMU course.

Cumulative activity to date:

• 89 proposals submitted across four funding cycles
• 37 projects funded for 35 faculty and 4 graduate students teaching courses in all 7 CMU Schools/Colleges, including the CMU-Qatar and CMU-Silicon Valley campuses
• 32 projects implemented

Activity in AY 2025-26:

• 4 previously funded projects implemented
• 12 new proposals submitted, 5 projects funded

Writing Accountability Groups (WAGs)

• Provided writing support, accountability, and feedback to 20 faculty and graduate student recipients of GAITAR and PITF fellowships.
• 13 participants completed manuscripts during the 10-week program and submitted them to peer-reviewed outlets by June 30.