Journal of Computing in Higher Education

With the proliferation of generative artificial intelligence (AI) tools, a growing body of research reports negative associations between the use of these technologies and students’ critical thinking. However, the psychological pathways that may account for this association remain insufficiently understood. This study examines the relationship between university students’ generative AI use and their critical thinking in AI-usage contexts, and tests whether the perception of automation bias and cognitive offloading statistically mediate this relationship. A cross-sectional survey was administered to 526 undergraduate students recruited from three universities in western Türkiye. Generative AI usage intensity and critical thinking in AI usage were measured with previously published instruments; the automation bias perception and cognitive offloading measures were developed for this study across two independent pilot samples, and their psychometric properties are reported in detail. Structural equation modeling indicated that generative AI usage intensity was negatively associated with critical thinking in AI contexts, with a total effect (β = − 0.40, p < .001) and a substantially smaller direct effect (β = − 0.12, p = .014). Indirect paths accounted for a considerable portion of the total association through automation bias perception (β = − 0.15, p < .01) and cognitive offloading (β = − 0.13, p < .01). The model accounted for 50% of the variance in critical thinking in AI usage. Because the design is cross-sectional, these results describe patterns of association rather than causal or temporal sequences; alternative model specifications, including a reverse ordering, fitted the data comparably well. Instructional strategies targeting trust calibration and deliberate cognitive engagement are discussed as hypotheses for future intervention research.

Augmented Reality (AR) and Mixed Reality (MR) technologies present promising opportunities for enhancing anatomy education, for example by supporting learner-generated drawing tasks. This study investigated the impact of AR- and MR-supported drawing on drawing performance, learning outcomes, and the overall learning experience, in comparison to traditional instructor-guided drawing. A total of 73 medical students were randomly assigned to one of four conditions: traditional drawing (Control), AR-supported drawing, MR-supported drawing, and MR with stereoscopic 3D visualization. The results showed that both AR- and MR-supported drawing conditions enhanced intrinsic motivation, improved drawing accuracy and were associated with lower extraneous cognitive load. However, no significant differences in knowledge acquisition were found across the four groups. Interestingly, in the stereoscopic 3D visualization condition, learners with higher intrinsic motivation demonstrated poorer learning performance. This may indicate that their attention was directed toward interacting with the system rather than conceptual understanding. Furthermore, visuospatial ability and prior knowledge moderated some learning outcomes across conditions, with more experienced learners gaining greater benefits. These findings underscore the importance of evaluating both learning performance and learner experience. They also emphasize the need to tailor AR/MR-based educational tools to individual learner characteristics in order to maximize their educational impact.

Discussions on Generative AI (GenAI) and student learning have shifted from whether students should be permitted to use GenAI to how educators and institutions may support its use to enhance learning. Specifically, understanding the factors associated with students’ expedient and instrumental use of GenAI for help-seeking is essential. However, less is known about the motivational factors that shape these distinct patterns of AI use. Using an expectancy-value-cost framework, we explored which motivational factors were associated with expedient and instrumental GenAI help-seeking among 389 college students studying in STEM disciplines. We used a latent profile approach to create various profiles of STEM-specific motivational beliefs. Profile membership predicted expedient and instrumental AI help-seeking in several interesting ways. Overall, profiles with higher cost perceptions towards STEM coursework were less inclined to use AI in more mastery-focused ways (lower AI instrumental help-seeking) and more inclined to use AI as a shortcut for completing assignments (higher AI expedient help-seeking). Implications for theory, practice, and future research are discussed.

As the demand for cultivating students’ higher-order thinking skills increase, group assessment in online collaborative learning, which is a learning strategy integrating collaborative learning and peer feedback, becomes more necessary for improving students’ learning performance and higher-order thinking abilities. However, in assessment practices, this learning strategy faces challenges due to inconsistent quality of group assessments, students’ inadequate evaluative competence, and low willingness to participate. Recently, generative artificial intelligence (GenAI) has provided support for addressing these problems. This study designed a GenAI-supported online group assessment teaching model and conducted a quasi-experimental study in a university elective course. Thirty students participated in the study and were divided into 10 groups. The results showed that GenAI-supported group assessment could significantly enhance students’ task performance, feedback quality, and critical thinking. However, the study also found that using GenAI tools in group assessment somewhat undermined the students’ generation of novel ideas during group discussions. The findings revealed that when adopting GenAI tools in group assessment, it is essential to maintain a proper balance between technical assistance and learners’ independent thinking. This conclusion lays theoretical foundations and offers practical implications for promoting the deeper integration of GenAI into the teaching process.

In this study, we examine rich clubs—tightly connected groups of highly active participants—in online discussion boards. Using a combination of social network and discourse analysis, we analyzed social and cognitive engagement patterns in five undergraduate courses. Based on the analysis of approximately 3000 posts, we found that rich clubs form even in small-scale networks, with rich club members exhibiting high levels of interaction. However, discourse analyses revealed only marginal differences in the quality of discourse between rich club and non-rich club members. Based on our findings, we suggest that rich clubs may indicate bottlenecks to information flow, and we suggest ways to foster participatory discussion boards by integrating designed prompts that encourage early posting and scaffolded participation. We also suggest further exploration of rich club discourse using a connected, interactive stance.

The expansion of online higher education has generated increasing demand for effective strategies to support faculty professional development. Among these, peer mentoring mediated through structured class observation and rubric-based evaluation has emerged as a promising practice to enhance teaching quality in virtual learning environments. This study examines the impact of a formative peer mentoring program with assigned roles implemented at an online university in Spain. A pre-experimental pretest-posttest design was applied with a sample of 90 faculty members from the School of Education. Each participant was observed by an expert mentor through the analysis of recorded synchronous online classes, using a sequential rubric designed to evaluate five key dimensions of online teaching: pedagogical design, communication, on-screen presence, instructional design and facilitation. Descriptive statistics, Wilcoxon signed-rank tests, and qualitative coding of observations were conducted. Results showed statistically significant improvements across all evaluated dimensions, with particularly strong effects in Learning Facilitation (r = 0.891) and Communication (r = 0.820). The qualitative analysis revealed a notable reduction in corrections associated with pedagogical weaknesses, as well as increased pedagogical awareness and the adoption of more effective teaching strategies. Structured mentoring supported by professional feedback demonstrates high potential to enhance pedagogical practice in virtual environments. This study provides empirical evidence of its positive impact on faculty development and offers an institutionally feasible approach to fostering a culture of continuous improvement, engagement, and evidence-informed reflection.

Generative AI is increasingly positioned not only as a tool for accelerating production but also as a collaborator that reshapes how individuals regulate cognition, enact innovation, and negotiate creative integrity. This study developed a dual-mediation framework to explain how AI-assisted tool use, hereafter referred to as AI engagement, is associated with higher-order competencies. Drawing on Social Cognitive Theory, we conceptualized self-regulated learning (SRL) and innovative behavior (IB) as behavioral pathways linking AI-assisted tool use to critical thinking (CT) and creative integrity (CI). Survey data from undergraduate students in design-related higher education were analyzed using partial least squares structural equation modeling (PLS-SEM). The results showed that AI-assisted tool use was positively associated with SRL and IB, which were in turn associated with CT and CI. Significant indirect effects further indicated that reflective regulation and purposeful innovation served as important mechanisms through which AI-assisted engagement was linked to higher-order growth. A complementary qualitative phase illustrated how learners framed AI as a “cognitive partner” for idea clarification, iteratively repurposed AI-generated outputs for innovation, and grappled with questions of authorship and attribution. By integrating quantitative modeling with qualitative insights, the study reframes AI not merely as a productivity booster but as a catalyst for reflective, innovative, and ethically attuned practices in AI-mediated design contexts. These findings provide context-specific evidence for understanding human–AI collaboration and offer practical directions for embedding responsibility and criticality in AI-mediated learning environments, while advancing a process-oriented account of how learners translate AI-assisted engagement into cognitive and ethical development.