Category: Higher Education

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Higher Education Language Development

Task Offloading to GenAI in the Writing Feedback Process and Its Effects on Writing Development

Lai and colleagues used a quasi-experimental design to investigate whether offloading different tasks to GenAI in the “draft–assess–revise” workflow affects English-as-a-foreign-language (EFL) students’ writing development. Conducted over seven weeks, the study involved 101 Chinese university students and compared three conditions: (1) GenAI drafting, student assessment, student revision; (2) student drafting, GenAI assessment, student revision; and (3) student drafting, student self-assessment, student revision.

Results showed that the GenAI drafting group performed best in writing ability (M = 18.21, SD = 2.21), significantly higher than both the GenAI assessment group (M = 16.30, SD = 1.70) and the no GenAI group (M = 15.15, SD = 1.79). This indicates that including GenAI in the feedback workflow generally benefits writing, but offloading the drafting task rather than the assessment task produces the strongest effects.

Regarding cognitive processes, the GenAI drafting group showed significantly higher cognitive engagement, utilizing more total prompts (median 20.50 vs. 9.00, U = 195, p < .001) and more learning-oriented prompts (median 10.50 vs. 8.00, U = 398.50, p < .05) than the assessment group. They also outperformed the other two groups in metacognition in problem identification (M = 4.41), argumentation (M = 2.82), and providing constructive feedback (M = 4.97) during peer review.

In writing self-efficacy, the GenAI drafting group demonstrated the largest improvement (M = 4.74, SD = .55), significantly higher than the other groups, whereas the GenAI assessment group (M = 4.42, SD = .53) and no GenAI group (M = 4.45, SD = .78) did not differ significantly. Interviews revealed these students experienced a stronger sense of control and competence because they could critically evaluate and revise AI-generated drafts. In contrast, some students in the GenAI assessment group worried about overreliance on AI feedback, leading to “false confidence.”

Overall, the study highlights that the educational value of GenAI in writing feedback depends on which tasks are offloaded to AI. Offloading the drafting task to GenAI while having students handle assessment and revision promotes deeper cognitive engagement, stronger metacognitive understanding, and higher self-efficacy. The authors recommend that writing instruction should prioritize AI-generated drafts paired with student evaluation and revision, serving as a catalyst for critique and reflection rather than replacing student thinking.

Source (Open Access): Lai, C., Pan, M., Guo, K., & Cui, Y. (2026). What task to offload to GenAI in the writing feedback process? – Effects of task-offloading approaches on EFL learners’ writing skill development. Computers & Education, 252, 105675.

https://doi.org/10.1016/j.compedu.2026.105675… Read the rest

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Achievement Higher Education

Student agency in Colombian higher education: a dual-pathway model of mediation and moderation between student engagement and academic achievement

A recent quantitative study by Torres Castro and Pineda-Baéz examines the relationship between student engagement and academic achievement, with particular attention to how student agency functions as both a mediating and a moderating mechanism in this relationship.

The study surveyed 1,713 final-year students from six accredited private universities across five regions in Colombia. Grounded in social cognitive theory and the ecological model of student engagement, the study conceptualizes student engagement as a multidimensional construct. It encompasses ten indicators across four domains: academic challenge, learning with peers, experiences with faculty, and campus environment. Student engagement is assessed through the National Survey of Student Engagement, with scores standardized on a 0 to 60 scale. Student agency is operationalized as agentic engagement through the five-item Agentic Engagement Scale. This scale captures proactive behaviors such as expressing preferences, asking questions, and suggesting adjustments to instruction. Academic achievement was measured by self-reported cumulative grade point average on Colombia’s standardized 0.0 to 5.0 scale. The study employed hierarchical regression with robust standard errors, structural equation modeling, and bootstrap-based mediation analysis using R version 4.3.1.

The findings reveal that among the ten engagement indicators, only collaborative learning and student-faculty interaction are positively and independently associated with academic achievement. Among the five agentic engagement behaviors, only expressive voice emerged as a significant mediator, channeling approximately six percent of the total effect of collaborative learning on grade point average. Expressive voice also moderated the relationship between student-faculty interaction and grade point average in a compensatory pattern. The association between faculty interaction and achievement was stronger among students with lower levels of expressive voice, whereas it was attenuated yet remained positive among those with higher levels. Although effect sizes were modest, the findings demonstrate that student agency operates as a context-dependent dual mechanism.

The study further suggests that instructional practices should combine structured guidance with opportunities for student-initiated contribution, and that support systems should prioritize students with lower levels of agentic engagement.

Source (Open Access): Torres Castro, U. E., & Pineda-Baéz, C. (2026). Student agency in Colombian higher education: a dual-pathway model of mediation and moderation between student engagement and academic achievement. Higher Education, 1-20.

https://doi.org/10.1007/s10734-026-01669-3… Read the rest

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Higher Education Language Development

Comparing the effects of ChatGPT and automated writing evaluation on students’ writing and ideal L2 writing self

Using a randomized controlled experimental design, Shi et al. (2025) compared the effects of ChatGPT-based feedback and traditional automated writing evaluation (AWE) systems on English-as-a-foreign-language (EFL) students’ writing performance and their ideal L2 writing self. One hundred and fifty second-year university students from three writing classes in a Chinese public university were recruited and randomly divided into a ChatGPT group, an AWE group, and a control group.

After an eleven-week intervention, results showed that ChatGPT helped students perform better in their writing compared to the control group and the AWE group, but compared to the AWE group, ChatGPT significantly lowered students’ ideal L2 writing self. Qualitative results shed light on possible causes: while participants were fully aware of the affordances of ChatGPT feedback, they were also concerned with their (over) reliance on the tool and the accompanying loss of creativity and agency and expressed their reserved attitude toward future intention to use ChatGPT.

Educators should refine learning objectives based on students’ ZPD and design prompts accordingly, so that ChatGPT supports learning rather than completing tasks, while also teaching prompt-engineering skills. For lower-intermediate to intermediate learners, AWE’s systematic and rule-based feedback can provide stronger scaffolding and better preserve authorship. However, ChatGPT’s richer affordances may lead to over-reliance, weakening learner agency and diminishing the ideal L2 writing self. Therefore, language-education goals should be redefined to incorporate AI literacy and critical thinking, safeguarding teacher and learner agency and promoting responsible use.

 

Source (Open Access): Shi, H., Chai, C. S., Zhou, S., & Aubrey, S. (2025). Comparing the effects of ChatGPT and automated writing evaluation on students’ writing and ideal L2 writing self. Computer Assisted Language Learning, 1-28.

https://doi.org/10.1080/09588221.2025.2454541… Read the rest

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Higher Education

The Role of Undergraduates’ Critical Thinking in Generative AI Reliance Behaviors

Hou and colleagues conducted a large-scale survey study using structural equation modeling (SEM) to examine how undergraduates’ critical thinking influences their different types of reliance on generative AI during problem-solving tasks. The study analyzed 808 valid responses, measuring students’ critical thinking skills and dispositions, AI literacy, trust in AI, and four types of AI-use behaviors—reflective, cautious, collaborative, and thoughtless use. The authors conceptualized reliance behavior as the way learners evaluate and make use of the differences between AI and human abilities, and proposed that critical thinking may play a key moderating role in this process.

The results showed that AI literacy strongly predicted both critical thinking skills (β = .66, p < .001) and dispositions (β = .41, p < .001), whereas trust in AI was negatively related to both (skills: β = –.16, p < .05; disposition: β = –.11, p < .001). Regarding reliance behaviors, critical thinking skills were positively associated with collaborative use (β = .25), reflective use (β = .21), and cautious use (β = .24), with similar effects found for critical thinking disposition. These findings highlight the importance of critical thinking in supporting desirable forms of AI use. In contrast, trust strongly predicted thoughtless use (β = .47, p < .001) and also slightly increased collaborative use (β = .15, p < .05) and reflective use (β = .19, p < .001), indicating a dual role of trust in both strengthening and weakening ideal reliance behaviors. More importantly, AI literacy promoted collaborative (β = .25), reflective (β = .20), and cautious use (β = .22) through the mediation of critical thinking, whereas trust produced negative indirect effects on these desirable behaviors because it reduced critical thinking (β = –.05 to –.06, p < .001). This means that critical thinking both enhances the positive influence of AI literacy and suppresses the potential blind reliance brought by high trust, guiding learners toward more reflective, careful, and collaborative ways of using AI.

Overall, the study provides strong evidence that critical thinking does not simply reduce AI reliance; instead, it shapes how students rely on AI, encouraging forms of use that are more reflective, collaborative, and prudent. The authors argue that the development of AI literacy must be accompanied by the cultivation of critical thinking to reduce thoughtless dependence and to promote healthier human–AI collaboration. They also emphasize that educational interventions should clearly define “ideal reliance behaviors” and help students develop responsible and thoughtful habits of AI use in an era where generative AI is becoming increasingly widespread.

Source (Open Access): Hou, C., Zhu, G., & Sudarshan, V. (2025). The role of critical thinking on undergraduates’ reliance behaviours on generative AI in problem‐solving. British Journal of Educational Technology, 56(5), 1919-1941.

https://doi.org/10.1111/bjet.13613… Read the rest

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Higher Education Social and Motivational Outcomes

Learners’ Preferences for Feedback from AI and Human Instructors

Le and his team examined whether learners’ preferences for feedback from human instructors versus generative artificial intelligence (AI) would change after receiving feedback from different sources and interface types in an academic English writing task. The study recruited 114 university students who were non-native English speakers and randomly assigned them to four groups: no feedback (control), human instructor feedback, ChatGPT 4.0 in a free-conversation interface, and a structured writing analysis tool powered by ChatGPT. Learners’ preferences were measured both before and after the task using rating scales and binary-choice questions, and the four groups were compared in terms of post-task preference and preference change.

The results showed that learners already had a clear preference for human instructors before the task (87.2% chose human), and this preference remained stable after the task (86.0% chose human), reflecting a phenomenon of algorithm aversion in educational settings. However, post-test preference scores differed significantly among the four groups: the human instructor group rated significantly higher than both the free-conversation AI group and the control group. On the binary human/AI choice measure, significant differences were also found — the human instructor and structured AI tool groups both scored higher than the free-conversation AI group. Regarding preference change, the overall mean shift was close to zero, but the differences among groups were significant: the free-conversation AI group showed a slight increase in preference for AI, whereas the human instructor and structured AI tool groups remained more favorable toward humans. In other words, although all three feedback types were effective, the free-conversation interface was the only one that reduced algorithm aversion and increased learners’ acceptance of AI, while the structured, one-time feedback tool further reinforced their preference for human instructors.

Based on these findings, the authors argue that enhancing the interactivity and dialogic nature of AI-based learning tools may influence learners’ preferences more effectively than purely improving their technical performance. Interactive dialogue allows for clarification and correction, which reduces learners’ unrealistic expectations that algorithms must be perfect and mitigates distrust. Overall, the study situates human preference within the context of interface design, providing both empirical insights and cautions for the adoption, product design, and pedagogical integration of AI in education.

 

Source (Open Access): Le, H., Shen, Y., Li, Z., Xia, M., Tang, L., Li, X., … & Fan, Y. (2025). Breaking human dominance: Investigating learners’ preferences for learning feedback from generative AI and human tutors. British Journal of Educational Technology.

https://doi.org/10.1111/bjet.13614… Read the rest

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Achievement Higher Education

How International Roommates Shape Academic Success in Low-Income Colleges

Tsai and Trinidad (2025) investigate how intercultural roommate pairings affect college outcomes among low-income U.S. students. The study centers on Berea College, a tuition-free liberal arts institution in Kentucky that primarily serves economically disadvantaged populations. Leveraging institutional data from over 6,600 domestic students between 2000 and 2015, the authors evaluate whether being paired with an international roommate in the first year influences academic performance and persistence throughout college.

Using quasi-experimental methods, including inverse probability weighting (IPW) and robustness checks through augmented and matching estimators, the researchers compare students with and without international roommates while accounting for demographic factors such as race, gender, and transfer student status. The findings reveal that domestic students paired with international roommates achieved significantly higher first- and second-year GPAs (approximately 0.14 and 0.10 points higher, respectively) and showed a modest improvement in second-year retention (about four percentage points). However, the benefits gradually diminished over later years, and no significant effects were found for long-term persistence or six-year graduation rates.

The authors interpret these results through the lens of peer effects and diversity in higher education. They suggest that intentional intercultural roommate pairings create structured opportunities for cross-cultural engagement that may counteract homophily in predominantly white, low-income settings. This exposure not only enhances academic habits and motivation but also fosters inclusivity and openness among domestic students.

Overall, the study provides empirical evidence that low-cost, policy-driven diversity interventions, such as pairing domestic and international students, can meaningfully improve early academic outcomes for disadvantaged students. While the effects do not extend to graduation, the research highlights the importance of designing inclusive residential environments that promote sustained intercultural interaction as a pathway toward educational equity.

 

Source (Open Access): Tsai, H. T. A., & Trinidad, J. E. (2025). Effect of International Roommates on College Outcomes: Evidence from Students of Disadvantaged Backgrounds. Educational Policy, 08959048251315481.

https://doi.org/10.1177/08959048251315481… Read the rest