Journal of Computing in Higher Education

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.