Journal on Excellence in College Teaching
The Journal on Excellence in College Teaching is a peer-reviewed journal published at Miami University by and for faculty at universities and two- and four-year colleges to increase student learning through effective teaching, interest in and enthusiasm for the profession of teaching, and communication among faculty about their classroom experiences. The Journal is published four times a year.
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Studying a Moving Target: Generating Evidence on AI’s Impact on Teaching and Learning
We desperately need research evidence on AI-enhanced pedagogy. And we may never be able to generate it fast enough.
Copyright (c) 2026 Journal on Excellence in College Teaching
Reflections From the Editors: Reimagining Learning Outcomes Through AI-Enhanced Pedagogy
With its 40 years of history, the scholarship of teaching and learning (SoTL) is an established approach for generating evidence in teaching and learning. Yet little is known about doing SoTL on AI-enhanced pedagogy. This reflective synthesis of the studies in this special issue reinforces the importance of using SoTL to generate transparent and replicable evidence. The editors used a systematic approach to synthesize the issue’s articles and combined it with a critical reflection model to deepen our understanding of the connections between evidence on effective AI use in teaching and learning and its implications for the field.
Evaluating Various Large Language Models’ Scoring Characteristics of Open-Ended Responses and Essays
This study evaluates the grading effectiveness of large language models (LLMs), including ChatGPT-5, Claude, and DeepSeek, on open-ended responses and essays. In Phase 1, AI scores were compared with scores of human instructors, revealing differences in leniency, depth, and alignment, quantified using a normalized distance metric. In Phase 2, prompt engineering and few-shot learning improved alignment with human graders. Principal Component Analysis plots with Kernel Density Estimation contours supported these gains. A single middle-performing exemplar strategy consistently improved grading alignment across models and assignments without negative effects, while other strategies showed variable results. Careful design remains essential to ensure fairness and responsible AI-assisted assessment.
: grading reliability, large language models (LLMs), prompt engineering
Copyright (c) 2025 Journal on Excellence in College Teaching
Using Advocacy Exercises to Promote Academic Identity and Ownership
Many science students leave their discipline before graduation. To combat this trend, educators use laboratory experiences and independent research to foster positive scientific attitudes. A non-laboratory Neuroscience pilot course was structured around advocacy to determine if advocacy-themed exercises would also enhance student attitudes. At the end of the semester, students in the advocacy course, but not a comparable control course, scored higher on the Persistence in the Sciences (PITS) questionnaire. This result suggests that advocacy efforts can enhance students’ academic identity and ownership. Further studies within and across disciplines are needed to determine the full extent of these effects.
From Accommodation to Empowerment: The Risks and Benefits of GenAI for Neurodiverse College Students
While the potential for GenAI in the academic environment is extensively debated, the discussion largely excludes the impact of this technology on the neurodiverse population. Current AI research is focused on the neurotypical population, with little consideration for neurodiverse students. While there is an opportunity for AI to be used as a personalized support system and a powerful contributor to a classroom founded on UDL, this cannot be accomplished without instructor support and guidance along with the careful incorporation of feedback. Careful policy creation and classroom implementation are required to best meet all student needs.
college teaching; generative AI; neurodiversity; universal design for learning; assistive technology
Generative AI in College Writing: Critical Literacies, Student Perspectives, and Strategies for Authorial Balance
Drawing from interviews with undergraduates, this article offers a critical analysis of the problems with generative artificial intelligence (GenAI) and student-centered recommendations for approaching college writing and GenAI. Data presented include examples of how students work with GenAI, showing critical engagement with writing processes, the challenges created by non-AI use policies, and considerations of overreliance, examining how students negotiate writing with GenAI. Recommendations include the benefits of an invitational, trust-centered pedagogy, the importance of fostering critical literacies about GenAI, the need to articulate what overreliance looks like for specific assignments, and strategies for helping students find authorial balance.
Using a Chatbot to Assess: Application of a Conversational Tool in an Assessment Course
The authors describe the development of an AI chatbot, based on a large language model (LLM), as a formative assessment tool in a master’s-level educational assessment class. The pedagogical goal was to provide an experiential assessment and feedback on student content knowledge. The students and instructor found the conversational assessment straightforward, but were concerned about feedback quality, grading reliability, and development time. For use in classroom assessments, LLM-based chatbots and related applications will need guidance from educators on appropriate conversational responses. Educators will also need to develop verification methods regarding the reliability of chatbot-assigned grades.
Permitted or Prohibited? Mapping AI Use Across University Disciplines
Examining the implementation of AI policies in different disciplines in the postsecondary context is relevant for understanding this changing terrain. This study examines potential disciplinary differences between postsecondary educators’ strictness level in the adoption of AI policies in their courses and use of generative AI in policy creation, using a publicly available dataset that describes syllabi policies for generative AI use at a variety of institutions in the United States. Implications from this study offer insight into AI integration and use based on broader discussions of levels of strictness and ethics in higher education.
Supporting Preservice Teachers in Eliciting Student Mathematical Thinking Through AI-Enhanced Pedagogy
This study redesigned mathematics coursework to strengthen elementary teacher candidates’ diagnostic competence using AI-generated cases of children’s thinking. Three courses were restructured with brief, progressive tasks focused on eliciting, interpreting, and responding to student reasoning. Compared with teacher candidates in non-AI-enhanced courses, those in AI-enhanced courses demonstrated stronger pedagogical reasoning and were more likely to elicit student thinking than to correct it.
The Role of Emotions and Beliefs in Shaping How Students and Instructors Navigate GenAI
As higher education navigates the impacts of generative artificial intelligence (GenAI), understand-ing student and instructor perspectives is critical. This study surfaced instructor and student emo-tions and beliefs to interpret their stances toward GenAI. Building on paradigms from Fawns’s (2022) entangled pedagogy, the authors investigated how student and instructor perspectives of GenAI aligned with these paradigms. The results reveal polarized attitudes toward GenAI common in both groups that reflect perceived hierarchies between technology and pedagogy. Findings sug-gest that ambivalent or hopeful emotional states and awareness of the diversity of learner-technology relationships enables a nuanced understanding of GenAI’s possibilities.
Doctoral Students’ Beliefs and Practices About GenAI: A Case Study of One Doctoral Program
Generative artificial intelligence (GenAI), which has emerged as a widely utilized tool in doctoral programs in educational leadership, presents potential benefits, risks, and ethical dilemmas (Kumar & Gunn, 2024). Reconciling faculty expectations with students’ uses of AI has emerged as a daunting challenge (Boyd & Harding, 2025). This article describes doctoral students’ knowledge, use, perceptions, and concerns about GenAI with the program’s informal and formal policies and practices for both coursework and dissertations. Results of a program-wide survey of current doctoral students and alumni are discussed in relation to programmatic expectations and guidelines. Implications for practice are provided.
Learning With Generative AI Through Custom Chatbots
This design-based study chronicles a four-semester process of developing, refining, and evaluating custom AI chatbots built with OpenAI for marketing education. Across fall 2024–fall 2025, iterative classroom pilots revealed how to address ethical and equity issues (usage limits, hallucinations), reduce cross-domain confusion, and build scalable multi-persona chatbots for guided inquiry. Quantitative results showed significant learning gains from traditional lecture-based instruction to one supplemented by personalized AI-supported tutoring, while POGIL and tutor chatbots produced comparable learning outcomes. The study offers a step-by-step guide for building, deploying, and analyzing AI chatbots to support equitable, responsible, and theory-informed active learning.
Impact of Using Generative AI as a “Feedback Partner” on Students’ Writing Quality
The authors examined the impacts of generative artificial intelligence (GenAI) as a “feedback partner” on students’ writing quality and self-efficacy for producing claims with supporting evidence. They assigned approximately students across first-year writing courses to four conditions (one peer review and three GenAI feedback conditions). In all conditions, performance and self-efficacy increased on second drafts. Writing skills improved most when students received multiple rounds of GenAI feedback. Subsequent instructor feedback further increased performance. Students receiving peer feedback reported a more positive experience than those receiving GenAI feedback. The authors discuss practical implications for writing instruction and teaching with GenAI.
generative AI; SOTL; writing instruction
Engaging a Faculty Learning Community for Incorporating Artificial Intelligence in Graduate Teaching
The accelerated growth of artificial intelligence (AI) and its impact on teaching and learning is undeniable (Bowen & Watson, 2024). Yet opportunities for faculty to engage in discipline-specific and multidisciplinary dialogues about AI-infused pedagogical strategies are limited (Cabellos et al., 2024; Shata & Hartley, 2025). This article will describe the constructive potential of a faculty learning community (FLC) for developing the integration of AI within courses and programs (Baidoo-Anu & Ansah, 2023), illuminating three examples from this FLC.
Integrating Generative Artificial Intelligence Technologies Across the Disciplines to Facilitate Learning and Responsible Usage
The authors describe the implementation of generative AI-aware course assignments in the humanities, social sciences, and STEM disciplines while preserving or augmenting course learning goals at a time when the technologies are pervasive. Instructors developed their course assignments during a seven-month long faculty fellowship program and focused on fostering students’ critical-thinking skills, scientific writing and communication, information literacy, storytelling, and gamification. Preliminary research on students’ attitudes toward GenAI revealed statistically significant increases in their reported willingness to use GenAI technologies after completing the activities. Many students described the value of using GenAI while simultaneously critically acknowledging challenges and limitations.
Designing Together: How Generative AI Can Support Faculty–Instructional Designer Collaboration
This article explores how generative artificial intelligence (GenAI) can enrich collaboration between faculty and instructional designers in online course development. Through personal reflection and a co-developed graduate course, we show how GenAI can support ideation, alignment, and creativity. Discomfort, we argue, is not only inevitable but essential to this work. When used intentionally, GenAI fosters curiosity, experimentation, and deeper dialogue. By reframing GenAI as a partner in meaning-making rather than automation, we invite teams to embrace course design as an evolving, relational process—where connection, not speed, becomes the foundation for innovation.
generative AI, instructional design, faculty-ID collaboration, course development
College Program for Academic Success: A Case Study on the Experiences of Students
This study examines the experiences of participants in a student success program at a midwestern U.S. university, focusing on the 2021 cohort. The program predominantly serves first-generation, non-traditional students and those from low socioeconomic backgrounds and marginalized communities, including Black, Indigenous, and People of Color (BIPOC). Through a case study approach, three core themes emerged regarding the barriers these students face in college: personal investment, self-doubt, and confusion. The findings highlight a disconnect between the services offered by the program and the students’ perceived needs, suggesting areas for improvement to better support student success.
Proving Provenance: Empowering Students to Validate Their Work to Avoid AI Academic Misconduct Accusations
This quantitative study examined how artificial intelligence (AI) has influenced academic misconduct cases reported by instructors. Findings show AI-related cases now surpass all other types of misconduct, with most reports involving computer science majors, males, and international students. The study recommends moving beyond unreliable AI detection tools and adopting innovative practices—such as requiring students to follow the Cole (2024) framework to verify the provenance of their assignments before submission—to promote fairness and accountability in academic integrity practices.
Artificial Intelligence; Course design; Academic misconduct; Proving provenance
Inspiring Students to Seek the Meaning of Global Citizenship in Their Lives: Lessons From Personal Pedagogy and Research
Inspiring students to seek the meaning of global citizenship in their personal lives is a critical responsibility of a higher education curriculum that aims to provide students with indispensable knowledge and skills to participate successfully in an increasingly pluralistic and interconnected global society. This article relates personal reflections from teaching and research to offer a view that higher education should provide all learners with a holistic global citizenship education. Both curricular and extracurricular activities are needed that intentionally cultivate the requisite knowledge and skills associated with global awareness, social responsibility, and engaged civic action.
“When They Are Real With Us”: Student Perceptions of Inclusive Teaching and Learning Practices
Instructors are encouraged to use inclusive learning strategies, but few studies examine student perceptions of these approaches (Cook-Sather et al., 2021). In this mixed-methods study, participants completed a 14-question survey assessing their perceptions of inclusive learning, including which strategies they consider inclusive and supportive of learning processes and which they consider exclusionary. Overall, participants noted that positive reinforcement, clear communication, and group-based learning support inclusivity, while instructor bias and poor communication make students feel excluded. Based on the findings, the authors recommend promoting inclusivity by being transparent, building clear expectations, recognizing personal biases, and facilitating a culture of grace.
Is All Active Learning the Same? Exploring Mathematically Derived Inquiry-Based Learning
This article explores inquiry-based learning (IBL) pedagogy, particularly in mathematics education, examining how it differs from problem-based learning (PBL) and case-based learning (CBL). IBL is defined as a student-centered approach involving sequenced problems or tasks that build engagement and understanding through group work. While IBL, PBL, and CBL share characteristics like problem solving and self-directed learning, each has distinct practices. The article presents a mathematics education community definition of IBL and provides detailed comparisons and examples to help readers understand IBL’s potential for broad application in scholarship beyond mathematics education.
A Collaborative Self-Study to Explore Assessment and Feedback in Online/Hybrid Courses
This collaborative self-study explored the teaching experiences of five faculty members who focused on elevating student experiences in their online/hybrid courses. The researchers were interested in assessment and feedback mechanisms to center students’ learning, de-emphasize grading, and engage in equitable practices. A basic qualitative approach uncovered themes throughout the semester of learning and grappling; stressors, uncertainty, and support/advice; and reflections on pedagogy and value of the approach. This work demonstrates the value of making small changes to pedagogical practices in online/hybrid courses and the value of a supportive cross-disciplinary reflective community of practice when making those changes.
self-study, online/hybrid teaching, Community of Practice, feedback
Who is Not Passing Statistics? Trends in an Undergraduate Statistics Course
A behavioral science statistics course is required for a number of majors at the author’s institution. Due to the challenging nature of the course, almost every semester there are students who do not earn a passing grade, which means that they must either retake it or find a new major that does not require it. The study found that students with lower SAT scores, lower class participation grades, and who are non-white did not pass statistics. The findings should aid colleges and universities in identifying and assisting students who are at risk of failure.
Pedagogy, Regression Analysis, Statistics
A Study of the Effectiveness of Using Team-Based Learning in College Courses
Building on previous research, the authors introduced formal team-based learning (TBL) to their classrooms and measured the quality of students’ team learning, professional development, and team satisfaction. They assessed students’ perceived fairness of work distribution and analyzed its effect on these outcomes and found that TBL resulted in increases in quality of team learning, professional development, and team satisfaction. Perceived fairness did not affect team learning or professional development. Team satisfaction was significantly related to perceived fairness, with students who perceived themselves as doing more work being more dissatisfied with the team experience. The authors provide suggestions for future research.
Team Based Learning, Professional Development, Team Satisfaction, Teamwork Skills
Building Connections With First-Year Students Through Peer Mentorship and Teachable Moments
To build connections among students studying education, the researchers implemented a First-Year Experiences (FYE) peer mentorship program with embedded professional development workshops called Teachable Moments for first-year students. This innovative approach to meeting the needs of first-year students was measured through a mixed-methods project using an open-ended survey and the coding of qualitative data from communication logs. Data revealed that the programmatic and curricular guidance and support provided to the first-year students led to the establishment of vital connections and positively impacted retention from their first to their second year.
First-year Experience, mentoring, mentees, mentors, professional development, connection, relationship, postsecondary outcomes, transitions
Critical Discourse Analysis of Disability Disclosure Statements in Academic Syllabi
This study used critical discourse analysis (CDA) to explore how course syllabi may impact disability disclosure in higher education. By examining 15 syllabi from across three disciplines at a public university, the analysis reveals the use of neutral but bureaucratic disability terminology and impersonal tones that may fail to foster an inclusive climate. These patterns may discourage disability disclosure by creating a distance between faculty and students. The study’s findings suggest that inclusive syllabi language affirming disability could encourage disclosure. Limitations include a small sample size and a lack of data triangulation. Further research incorporating faculty perspectives is recommended.
The Limits of Understanding: Lacanian Pedagogy in the Composition Classroom
This article presents a challenge to notions of clarity—and its relation to understanding—within the college composition classroom by relying on Lacanian pedagogical theory. While the field of composition has issued criticisms against clarity in student writing, there has been less emphasis on interrogating clarity in instruction. By delineating Lacanian pedagogy, the article provides a theoretical and pedagogical framework for integrating Lacanian non-sense into the composition classroom. Through this integration of Lacanian non-sense through metaphor, the author argues that a procedural knowledge that exceeds conventional notions of understanding and provokes thought can emerge.
Impact of Metacognition on Academic Performance in College Courses: A Systematic Review
This study contributes a systematic literature review of the effectiveness of incorporating metacognition instruction into college-level courses. The interventions examined were limited to course-level metacognitive strategies explicitly intended to enhance students’ academic performance, that is, graded assignments, course grades, or pass rates. Fifty-nine research studies involving 11,153 college students were analyzed. At least one statistically significant finding in which the intervention increased academic performance was found in 91.5% of the studies analyzed. These findings indicate that course-level metacognitive instruction is positively associated with improved academic performance, with positive correlations observed across various disciplines and geographic locations.
Exploring Student and Faculty Experiences in a HyFlex Learning Environment
Almost five years after the COVID-19 pandemic began, the higher education learning environment has been transformed, and research on this transformation is just starting to emerge. This article contributes to this effort by reporting the results of a study comparing two-course delivery formats: hybrid in-person and HyFlex. Results found no significant differences in student performance, attendance, or course ratings across formats. Results also revealed that students rate the HyFlex format favorably compared to other delivery formats. The authors examine the lessons learned and their implications for designing learning environments that are engaging, effective, and accessible.
Developing Culturally Relevant Continuous Case Studies for Child & Adult Advocacy: A Curriculum Development Project
Preparing mandatory reporters—those responsible for identifying and reporting abuse and neglect—requires innovative approaches. One effective method is the continuous case study. This article discusses the creation and implementation of culturally relevant continuous case studies within a multidisciplinary child and adult advocacy certificate program. Professionals from midwestern child welfare agencies created five case studies aligned with program learning objectives. Cases were used for student learning through multidisciplinary discussions, applying course content, and a virtual simulation for mandatory reporting and suicide prevention. The approach received positive feedback for its realism and contextual accuracy, fostering multi-system collaboration and trauma-informed care.
continuous case study, interprofessional, mandatory reporter training