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What’s the Eberly Center reading and thinking about this month?

The Research and Scholarship Digest, published the first Monday of each month, consists of short summaries of recently peer-reviewed studies on teaching and learning topics. This digest offers a view into what we are reading and thinking about at the Eberly Center that:

• adds to our understanding of how students learn
• is potentially generalizable across teaching contexts in higher education
• provokes reflection on implications for our teaching and educational development practices.

We hope the readers of this digest will find it a useful resource for staying in-tune with the rapidly expanding education research literature.

Fathi, Z., Zarei, P., Fong, C. J., Saha, A. K., & Sharmeen, S. (2026). The caring professor: A meta-analysis of associations between faculty-student relationships and postsecondary student success. Educational Psychology Review, 38(1), 8.

Faculty-student relationships (FSRs) have been shown to be predictive of student success in general, but less is known about FSRs in higher education. This meta-analysis of 128 effect sizes from 36 studies examines the relationship between FSRs and academic achievement and persistence in higher education using the effect measure r (correlation coefficient). Across all outcomes, a significant positive relationship was found (r = 0.18), with a stronger relationship for persistence (r = 0.33) than for academic achievement (r = 0.16 and r = 0.17 for course grades and GPA, respectively), although persistence was only measured by nine studies. Moderator analysis examining the six different dimensions of FSRs (caring attitude, approachability, personal connection, inclusivity, mutual respect, and encouragement) found that FSR measures that captured the caring attitude dimension showed a stronger association with student GPA. There was no evidence of moderation for any other FSR dimensions for any other outcomes, and there was no moderation from student gender or race. These results collectively suggest the link between FSRs and student success is important and requires more expansive research. The authors suggest faculty development programs should consider prioritizing relational pedagogy to better equip instructors for cultivating FSRs.

Guo, Z., Li, W., Chen, L. L., Liang, Q., & Wu, Y. (2026). Reconceptualizing digital literacy in the AI era: A systematic review of validated frameworks from an ecological perspective. Computers & Education: Artificial intelligence, 11.

This study examines how digital literacy should be reconceptualized in the AI era, using an ecological perspective that considers changes at the individual/educational, societal, and historical levels. The authors of this paper conducted a systematic review of 80 validated frameworks, consisting of 69 digital literacy and 11 AI literacy frameworks, published between 2014 and 2025. The review finds that digital literacy is expanding beyond traditional technical and information skills to include competencies such as AI ethics, critical evaluation of AI outputs, higher-order thinking, and human-AI communication and collaboration. At a micro level, the authors argue that “educators should act as active AI partners and design AI-resilient assessments, while students are expected to be ethical, responsible AI users and learners”. Socioeconomic differences are also studied: frameworks from developed regions tend to emphasize broader integrative competencies such as communication, collaboration, cultural considerations, and well-being, whereas frameworks from developing regions more often emphasize operational digital skills, some of which may increasingly be automated by AI. Overall, the article argues that AI is not simply adding a new skill set to digital literacy; it is changing what it means to be digitally literate.

Richardson, C. M. (2026). From code sprints to classrooms: Hackathons as experiential learning tools for engagement, collaboration, and graduate employability. Active Learning in Higher Education, 14697874261487163.

In this study, the researcher investigated Hackathon pedagogy as an intentional instructional approach for active, experiential learning within higher education. Moving beyond viewing code sprints as informal, extracurricular events, the study explores a curriculum-integrated model where postgraduate management students tackle real-world organizational datasets and present live solutions to industry executives. The core research questions explore how embedding hackathons directly into formal academic programs impacts student engagement, applied learning, peer collaboration, professional identity development, and overall graduate employability. Using a qualitative case study approach, the research gathers insights from postgraduate management students to evaluate both the benefits and pedagogical challenges of this approach. One of the challenges is aligning hackathons with formal assessment and academic modules. Transitioning hackathons into credit-bearing modules requires explicit integration with formal learning outcomes to provide academic legitimacy. To fairly recognize intensive student effort, educators must incorporate hackathon outputs into structured assessment frameworks, such as reflective portfolios, group presentations, and peer evaluations, while balancing creative autonomy with clear instructional guidance.

Zagami, J. (2026). Guarded adoption of generative AI in higher education: High-achieving students, successful-student identity, and epistemic agency in a single-university mixed-methods survey. International Journal of Educational Technology in Higher Education, 23(1), 47.

This study uses student survey and interview data from an Australian university to examine attitudes about generative AI use, beyond simple adoption or rejection. Although overall data about student attitudes may appear to indicate ambivalence, the researchers argue that students' decision to use generative AI or not is not simply pragmatic, but may be at least partially motivated by identity and epistemic agency. Students who identify as "successful students" may be more skeptical and critical about using these technologies, because of their belief that generative AI outputs may undermine their own capabilities and challenge their deeply-held values about autonomy and evidence of student success. Using student-reported GPA from 469 students, AI engagement, affect, and perceived positive learning as variables, they found that "higher-achieving students reported lower active AI engagement, positive affect, perceived learning impact, and AI-related disengagement, alongside slightly higher negative affect." Successful students most often choose to use AI "selectively for clarification, summarization, and workflow support," and critically verify outputs rather than using them indiscriminately. This study offers instructors insight into the types of tasks with which they may choose to ask students to engage with AI, and to consider the appropriateness of such tasks based on student level of intellectual development and expertise.