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Team Members

Jessica Sylvester,�� Melinda Kulick,��Chunfu Cheng,��Jill Van Allen,��Stella Smith

Abstract

The growing integration of generative artificial intelligence (GenAI) in higher education offers new opportunities for learner engagement and instructional innovation, yet its impact on graduate students in online master’s programs remains underexplored (Kim et al., 2025; Liang et al., 2023). This mixed-methods study examined how GenAI influences engagement across four dimensions—skill, participation, performance, and emotion—among 30 asynchronous, online, master’s students enrolled in College of Education (COE) programs who actively used GenAI in their coursework. Particular attention was given to this population as future educators and instructional leaders whose experiences with GenAI carry direct implications for classroom practice, technology integration, and leadership decision-making.

Grounded in Constructivist Learning Theory, Engagement Theory, and Self-Determination Theory, the study employed a concurrent triangulation design in which quantitative and qualitative data were collected simultaneously through an anonymous online survey. The quantitative component utilized the 22-item AI-Online Student Engagement (AI-OSE) scale. Internal consistency reliability coefficients ranged from .882 to .95 across engagement domains, indicating strong to excellent reliability. Descriptive statistics and one-sample t-tests were conducted to determine whether engagement levels significantly differed from a neutral midpoint.

The qualitative component consisted of open-ended survey responses exploring how students used GenAI and perceived its impact on engagement and learning. Thematic analysis, guided by Braun and Clarke’s framework and supported by ATLAS.ti software, identified recurring patterns in students’ experiences. Integration occurred through side-by-side comparison of quantitative and qualitative findings, generating meta-inferences that connected statistical trends with lived experiences.

Results indicated high levels of engagement overall, with notable program-level variance. Four key themes emerged: GenAI as cognitive scaffolding, productivity partner, institutional integration tool, and enabler of an AI-driven instructional ecosystem. Findings suggest GenAI enhances efficiency, confidence, and perceived learning outcomes; however, sustained emotional engagement and ethical use depend on intentional instructional design and structured guidance.

This study contributes empirical insight into GenAI’s multidimensional influence on graduate student engagement and highlights the importance of purposeful, pedagogically grounded integration to prepare future educators to adopt emerging technologies responsibly while fostering ethical, innovative learning environments..

Team Members

Melinda Kulick,�� Jessica Sylvester,��Chunfu Cheng,��Christina Bergren,��Jim Croushore�� ��

Abstract

Non-traditional online higher education students often persist under conditions of stress and limited support. Guided by phenomenological inquiry, this study was conducted to better understand student self-regulation, resilience, belonging, psychological safety, and generative artificial intelligence (GenAI) in digital learning environments. Using Braun and Clarke’s (2020) thematic analysis within a hermeneutic phenomenological framework, five themes emerged: stress as a constant companion, self-regulation as survival, resilience through struggle, belonging in digital spaces, and GenAI as a double-edged tool. Findings reveal sentiment and strategies within each theme that online learners confess to depending on for perseverance. Implications call for integrating trauma-informed pedagogy and GenAI scaffolding in counselor preparation and online higher education to promote resilience beyond survival.

Presentation

Bergren, C., Cheng, C., Croushore, J., Kulick, M., & Sylvester, J. (2025, October 16-18). The trauma informed approach: Enhancing academic success for online students [Conference presentation]. ������ý Knowledge Without Boundaries: Scholarship, Society, and Change, Phoenix, AZ, United States.

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Team Members:

Thom Sloan,��Amanda Gabarda,��Jennifer James,��Christopher R. Mosley,��Sisay Teketele�� �� �� �� ����

Abstract
��Artificial intelligence (AI) is increasingly reshaping healthcare delivery through advancements in diagnostics, treatment planning, patient management, and administrative efficiency. As AI technologies expand across clinical and operational domains, healthcare professionals must be prepared to navigate both their opportunities and challenges. This paper examines the need for integrating AI education into healthcare curricula to ensure graduates possess the knowledge, skills, and ethical awareness required in an AI-driven workplace. Drawing on current research and emerging best practices, the study highlights implications for professional roles and competencies, barriers to effective curriculum design, and strategies for embedding AI literacy into medical and health sciences education. The manuscript concludes with recommendations for aligning curricula to prepare healthcare professionals as competent, adaptive, and responsible users of AI technologies.

��Presentation

��Gabarda, A., James, J. Mosley, C.R., Sloan, T.J. & Teketele, S. (2026, March 12). Innovative strategies in advancing healthcare education. AECT Online Conference. https://virtual.oxfordabstracts.com/event/76038/program?date=%222026-3-12%22

Team members:

James Rice,��Suchitra Veera,��Susan Jones,��Anthony "Tony" Bennett,��Samantha Bietsch

Abstract

Integration of AI in the classroom has surfaced questions and challenges regarding academic integrity, ethics, and the effectiveness of educational chatbots. This quantitative study examined graduate students’ attitudes toward AI chatbots and their self-reported usage, with particular attention to perceptions of academic integrity, ethics, and educational value. Data were collected from 54 doctoral students enrolled at a private, online university in the United States using a structured survey instrument. Statistical analysis of group comparisons indicated no significant gender differences in attitudes toward AI chatbots, while significant differences were observed across fields of study. The antecedents - favorable attitudes toward chatbot use, perceived superiority of chatbot generated results, and disagreement towards prohibiting Chatbot use - were positively correlated with the reported frequency of ChatGPT usage. Findings highlight the need for discipline-sensitive guidance and clear institutional policies addressing ethical use in higher education.

Presentation

Bietsch, S., Bennett, A., Jones, R., Rice, J., Veera, S., Relationship between Students’ Attitudes towards Artificial Intelligence (AI) and their usage of AI Chatbots, 2025 Knowledge Without Boundaries Conference, Univ. of Phoenix.

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Team Members:

James�� Traylor,�� Robert�� King,�� Myrene Magabo,��Mark Guberman,��David Duren

Abstract

In today’s rapidly evolving educational landscape, the integration of artificial intelligence (AI) into classroom settings raises critical questions about creativity, purposeful design, and the preservation of human values. Central to this discussion is equity, which serves as the foundation for equality in learning. True equality in both in-person and virtual learning environments depends on equitable access to resources that are responsive to students’ diverse backgrounds, ages, and life circumstances. Data collected for this study indicate that learners perceive AI tools as beneficial to their educational experiences, underscoring the importance of ensuring equitable access to technological resources. When AI and related instructional tools are accessible to all students, there is a greater likelihood that students will perceive—and experience—fairness and inclusion in their learning environments. Beyond enhancing creativity, AI has the potential to serve as a powerful mechanism for promoting equity and inclusiveness in higher education classrooms.

Team Members:

Rex�� Holiday,�� Kelly Speed,��Neil Duchac��

Abstract
This literature review examines the evolving relationship between artificial intelligence (AI), literacy instruction, and the educational experiences of K–12 students with identified learning disabilities. Drawing on historical and contemporary scholarship, the review traces the development of AI and large language model (LLM) technologies in educational contexts and their growing integration into classroom-based edtech tools. The synthesis explores literacy instruction, the unique needs of learners with disabilities, and the ways AI-enabled tools may support or hinder equitable learning opportunities. The review also analyzes challenges associated with conducting AI-related educational research, including participant recruitment, access, consent, and variability in educator AI readiness. Findings offer implications for educators, researchers, policymakers, and edtech developers seeking to responsibly leverage AI to support literacy development for students with learning disabilities.

Team Members:

Jaime�� Januse,��Sushil�� Jindal,��Steve�� Gregoire

Abstract

To effectively integrate AI into education, university instructors need to have AI-specific technological, pedagogical, and content knowledge (TPACK). Research indicates that university instructors often feel unprepared with these competencies; thus, they lack the knowledge to effectively integrate AI tools into their teaching practices. While substantial literature exists on AI in education, there remains a significant gap in faculty-centered research, particularly regarding instructor knowledge, comfort, and ethical understanding of AI implementation in higher educational contexts.�� This study examined faculty perceptions regarding their AI-TPACK knowledge, ethics-based AI knowledge, and then assessed whether those perceptions were influenced by training in AI tools. Using a quantitative descriptive cross-sectional research design that is based on Celik’s TPACK framework, this empirical research examined faculty members’ self-perceived competencies in each area. The study found that AI-focused professional development significantly enhanced faculty knowledge and comfort with AI integration among the 100 faculty members who participated in the study.�� Also, the findings revealed a critical gap in ethical AI knowledge, underscoring the urgent need for ethics-specific training to build faculty confidence in classroom implementation.�� Universities should leverage these findings to offer targeted professional development initiatives that close identified gaps and improve instructional effectiveness and faculty digital literacy skills.

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Team Members:

Pam Darbyshire,��Carl Beitsayadeh,��Tiffani Hetrick

Abstract
��Generative artificial intelligence is increasingly shaping academic research workflows, offering innovative approaches to supporting dissertation literature reviews. While prior studies have examined AI capabilities, limited research explores doctoral students’ perceptions and experiences using these tools within structured academic settings. This qualitative exploratory case study investigates online doctoral students’ engagement with an AI-powered citation analysis and source discovery tool. Guided by the Technology Acceptance Model and Social Cognitive Theory, the study examines source selection, synthesis, and scholarly narrative development. Data were collected through course-based discussions, post-course surveys, and semi-structured interviews, and analyzed using Braun and Clarke’s six-phase thematic approach, supplemented with descriptive statistics. Findings will inform ethical and pedagogically sound integration of AI tools in doctoral education.

��Team Members:

Carl Beitsayadeh,��Pamayla Darbyshire��

Abstract

AI inference drives continuous, rising energy demand, yet most work centers on training. This cross-sectional secondary analysis benchmarks inference throughput and energy efficiency across public and private clouds using 739 standardized MLPerf v5.0 submissions. Energy efficiency was measured as system throughput normalized by vendor-reported thermal design power. Grounded in systems theory, efficiency is framed as an emergent property of hardware, infrastructure, and workload. Methods included Welch’s t-tests, one-/two-way ANOVA, and linear/log-linear regression with residual diagnostics and model fit. Results showed no cloud-type effect, significant accelerator differences (NVIDIA H200-SXM-141GB ~45% higher median efficiency), and no cloud–accelerator interaction. The Log-linear model best captured multiplicative, compound-growth-like scaling. The framework advances reproducible benchmarking and highlights inference as critical for sustainable AI.

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Rheanna Reed, DM,����Jennifer Carriere, Ph.D.,����Laura Pipoly,����Anthony Bennett, DM,��

Abstract

Online adjunct faculty encounter many unique challenges while continuing to play a pivotal role in higher education. These include burnout, high course loads coupled with long work hours, increased student numbers, excessive administrative work, lack of training, high academic competition, role ambiguity, and lack of wellness-promoting activities in higher education. These challenges could potentially set back student outcomes. Given the growing dependence on online adjunct faculty, delving into these complex dynamics is crucial. This research aimed to uncover the perceptions of the effectiveness of supportive best practices and to explore the evolution of these practices from the perspectives of adjunct faculty, university leaders, and archived documents like policies and procedures. This exploratory case study design offered a holistic view of support structures through the perspectives of online adjunct faculty, university leadership, and support departments using an open-ended questionnaire and archival documents. Thematic analysis and triangulation allowed patterns and themes to emerge regarding current practices' efficacy and pinpoint improvement areas. This increased understanding can lead to enhanced teaching practices, better student learning experiences, success, and consideration for best supportive practices within online higher education.

Publication

Reed, R., Carriere, J., Pipoly, L., & Bennett, A. (2025). From burnout to belonging: a sequential mixed methods study of comprehensive support structures for online adjunct faculty. Online Learning, 29(3), 103-129. https://doi.org.10.24059/olj.v29i3.5026

Presentation

Reed, R., & Carriere, J. (2025, November 18–20). Resilient & ready: Future-forward support for online adjunct faculty [Conference presentation]. OLC Accelerate 2025, Orlando, FL.

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Team members:

Jennifer L. James, PhD,��Karen Myers, DNP,��Olivia Miller, M.A.��

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Abstract

The rapid expansion of online higher education, accelerated by the COVID-19 pandemic, has raised concerns about academic rigor in virtual settings, especially for nontraditional students balancing work, family, and studies. Faculty perceptions of rigor—including cognitive demands, course management, and assessment of integrity—are vital for equitable, high-quality outcomes, yet evolving views can affect student engagement, retention, and program standards. This systematic literature review addresses the problem of inconsistent faculty perceptions of rigor in online teaching of nontraditional students, synthesizing peer-reviewed literature from 2018–2023 using PRISMA 2020 guidelines to inform better practices. Guided by the Community of Inquiry framework (emphasizing teaching, social, and cognitive presence for meaningful online learning), it examines: (a) shifts in faculty perceptions of rigor before, during, and after the pandemic; (b) rigor in course management; and (c) definitions of rigor for online faculty. Through thematic synthesis, key findings show perceptions of lower or harder-to-maintain rigor during the pandemic due to engagement and assessment challenges, with post-pandemic calls for faculty training to balance high expectations with support. Rigor is often defined as combining cognitive challenge, meaningful content, and structure, differing from traditional classroom norms. These insights support positive social change by informing faculty development, online course design policies, and equitable practices to boost access, retention, and success for nontraditional online learners.

Publication

James, J. L., Miller, O., & Myers, K. (2026). Studying faculty perceptions of rigor in online college courses: Compromising or accommodating? A literature review. Journal of Educators Online, 23(1).