Underlining what matters in higher ed and ed tech: news, vendor updates, and commentary from across the field, in one place.https://theunderline.netlify.app/research/https://osf.io/preprints/edarxiv/ydzcf_v1/https://osf.io/preprints/edarxiv/ydzcf_v1/In educational AI, “In-the-Loop” framings (e.g., human-in-the-loop) are presented as systems that secure human-technology balance in enabling timely feedback and personalization. This learning sciences perspective-piece argues that the critical issue is not whether humans participate sufficiently in educational…Tue, 13 Oct 2026 00:15:43 GMTPrajakt PandeResearchhttps://osf.io/preprints/edarxiv/er8dy_v1/https://osf.io/preprints/edarxiv/er8dy_v1/Artificial intelligence (AI) makes it increasingly possible to produce sophisticated intellectual outputs without undertaking the experiences that develop corresponding human capabilities. This weakens a practical assumption of traditional university teaching and assessment: that producing an academic output normally…Mon, 12 Oct 2026 19:50:31 GMTLe Truong TungResearchhttps://osf.io/preprints/edarxiv/gwfpx_v1/https://osf.io/preprints/edarxiv/gwfpx_v1/States invest heavily in producing technical graduates, yet the graduates most valued by an innovation economy are the ones least likely to stay; this study tests whether that pattern holds across credential levels, using an independent source as corroboration. Retention loss is measured by field and credential using…Mon, 12 Oct 2026 19:39:03 GMTAgelo ThalassinidisResearchhttps://doi.org/10.63608/ssj.4124https://doi.org/10.63608/ssj.4124Student happiness plays an important role in promoting academic engagement, well-being, and meaningful learning experiences in higher education. This study examined the level of learning happiness among students at the Ganesha University of Education (Universitas Pendidikan Ganesha), Bali, Indonesia, within the…Mon, 12 Oct 2026 12:00:00 GMTNi Luh Gede Erni Sulindawati et al.Researchhttps://doi.org/10.1007/s11423-026-10724-whttps://doi.org/10.1007/s11423-026-10724-wSun, 11 Oct 2026 12:00:00 GMTTongKai Guan et al.Researchhttps://doi.org/10.1007/s11423-026-10743-7https://doi.org/10.1007/s11423-026-10743-7Sun, 11 Oct 2026 12:00:00 GMTSeungmin Lee and Joongwon JeonResearchhttps://doi.org/10.1186/s41239-026-00628-3https://doi.org/10.1186/s41239-026-00628-3Abstract The rapid advancement of generative artificial intelligence is profoundly reshaping the paradigm of instructional design. However, the micro-level process patterns of human-AI collaboration in generative AI-enabled instructional design for pre-service teachers still require in-depth analysis. This study aims…Sun, 11 Oct 2026 12:00:00 GMTXu Fang et al.Researchhttps://doi.org/10.1007/s11423-026-10733-9https://doi.org/10.1007/s11423-026-10733-9Abstract A growing body of research on board games demonstrates that these games can function as learning environments and provide opportunities for learning across diverse contexts. Although learning is often conceptualized contextually within this literature, studies increasingly demonstrate that meaningful learning…Fri, 09 Oct 2026 12:00:00 GMTRebecca Yvonne BayeckResearchhttps://doi.org/10.1111/bjet.70084https://doi.org/10.1111/bjet.70084Abstract This study examines how social support and affective factors (enjoyment, boredom, embarrassment, and shyness) shape young learners' informal digital learning of Chinese (IDLC) in New Zealand. Drawing on proactive language learning theory, we conceptualized IDLC as learners' autonomous involvement with…Thu, 08 Oct 2026 12:00:00 GMTGuangxiang Liu and Xian ZhaoResearchhttps://doi.org/10.1007/s11423-026-10711-1https://doi.org/10.1007/s11423-026-10711-1Thu, 08 Oct 2026 12:00:00 GMTRebecca Griffiths et al.Researchhttps://osf.io/preprints/edarxiv/ve2f8_v1/https://osf.io/preprints/edarxiv/ve2f8_v1/Some courses limit how much generative-AI help students can obtain in assessed work, expecting scarcity to prompt careful use. A budget shared across tasks also requires students to hold resources back for later needs, and how this shapes their decisions is unclear. In a graded coding-repair task, 125 undergraduates…Wed, 07 Oct 2026 20:35:26 GMTLu Wang et al.Researchhttps://osf.io/preprints/edarxiv/ba69n_v2/https://osf.io/preprints/edarxiv/ba69n_v2/MOOCs, Smart Teaching (ST), and AI-assisted learning support different stages of higher education, yet a unified quantitative basis for analyzing their contributions, course design, and resource allocation remains limited. This study develops a mathematical model integrating MOOC-based pre-class learning, ST-based…Wed, 07 Oct 2026 19:47:28 GMTBo Yuan and Jiazi HuResearchhttps://doi.org/10.1007/s11423-026-10730-yhttps://doi.org/10.1007/s11423-026-10730-yAbstract Drawing on 11 evidence-based design principles, we developed a digital tool for training attention during academic activities. Two naturalistic, single-group pre-post studies—including a pre-registered direct replication—evaluated the impact of this tool on the attentional focus of adolescents across multiple…Wed, 07 Oct 2026 12:00:00 GMTMichael D. Mrazek et al.Researchhttps://doi.org/10.1007/s11423-026-10742-8https://doi.org/10.1007/s11423-026-10742-8Wed, 07 Oct 2026 12:00:00 GMTMeryem Polat Köseoǧlu et al.Researchhttps://doi.org/10.1016/j.iheduc.2026.101118https://doi.org/10.1016/j.iheduc.2026.101118Wed, 07 Oct 2026 12:00:00 GMTZiyi Kuang and Xiaoxue LengResearchhttps://doi.org/10.1080/00221546.2026.2742808https://doi.org/10.1080/00221546.2026.2742808Purpose This instrumental case study of organizational legitimation and socio-legal pressures investigates the (un)intended policy outcomes of Texas House Bill 51—a performance-based funding model.Research Questions Through critical policy analysis, this study asks how emerging research policy contexts exacerbate…Wed, 07 Oct 2026 12:00:00 GMTDwuana BradleyResearchhttps://arxiv.org/abs/2610.09460v1https://arxiv.org/abs/2610.09460v1As Large Language Models (LLMs) are increasingly adopted for automated grading and feedback in higher education, their structured outputs, including multi-dimensional rubric scores, detailed feedback comments, and improvement suggestions, create an appearance of thorough analytic evaluation. This study examines…Wed, 07 Oct 2026 05:17:26 GMTXi ZhaoResearchhttps://doi.org/10.1007/s11423-026-10739-3https://doi.org/10.1007/s11423-026-10739-3Tue, 06 Oct 2026 12:00:00 GMTPanagiotis Kosmas and Andria AgesilaouResearchhttps://doi.org/10.1007/s11423-026-10676-1https://doi.org/10.1007/s11423-026-10676-1Abstract The acquisition of science concepts is necessary for fostering analytical and problem-solving skills. The complexity of heart anatomy, obscured within the human body, presents a challenge that high-immersion virtual reality (VR) holds promise in addressing. This study explored the impact of VR versus…Tue, 06 Oct 2026 12:00:00 GMTRegina Kaplan‐Rakowski et al.Researchhttps://doi.org/10.1007/s11423-026-10722-yhttps://doi.org/10.1007/s11423-026-10722-yAbstract This study examined how a sequenced virtual reality (VR)–supported learning activity influenced learning and motivation in undergraduate engineering education. Drawing on cognitive load theory, generative learning theory, and the cognitive theory of multimedia learning, the study evaluated a desktop VR…Tue, 06 Oct 2026 12:00:00 GMTAdurangba Victor Oje and Nathaniel J. HunsuResearchhttps://doi.org/10.1007/s11423-026-10738-4https://doi.org/10.1007/s11423-026-10738-4Tue, 06 Oct 2026 12:00:00 GMTJill Erin StefaniakResearchhttps://arxiv.org/abs/2610.07483v1https://arxiv.org/abs/2610.07483v1The widespread availability of LLMs is challenging written learning activities, as students can increasingly generate responses without necessarily engaging with the learning content. Conversational AI creates an opportunity to redesign these activities as dialogue, while multilingual capabilities may make such…Mon, 05 Oct 2026 22:48:27 GMTDeepak Varuvel DennisonResearchhttps://arxiv.org/abs/2610.07205v1https://arxiv.org/abs/2610.07205v1Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and missing contextual information often affect their interpretation. To support more responsible interpretation of IA, we introduce FACTRIA, a framework that organizes…Mon, 05 Oct 2026 18:19:34 GMTFrancielle MarquesResearchhttps://arxiv.org/abs/2610.06532v1https://arxiv.org/abs/2610.06532v1Peer feedback promotes active learning, critical reflection, and skill development, but its effectiveness is often limited by the quality of feedback students provide. Recent advances in LLMs offer new opportunities to support peer feedback by generating more coherent and actionable feedback while preserving human…Mon, 05 Oct 2026 15:35:09 GMTAlvaro BecerraResearchhttps://arxiv.org/abs/2610.06506v1https://arxiv.org/abs/2610.06506v1We present an AI-based web-oriented tool designed to support formative feedback for oral presentation slides in higher education: AISSA. It allows students to upload their slides before presentation and automatically receive rubric-based quantitative scores and qualitative feedback generated by large language models…Mon, 05 Oct 2026 15:25:06 GMTAlvaro BecerraResearchhttps://doi.org/10.14742/ajet.10808https://doi.org/10.14742/ajet.10808Students have faced substantial challenges concerning belonging following the pandemic, particularly related to isolation and loneliness. This study implemented and examined the efficacy of an intervention using personalised emails and learning management system (LMS) based promotional videos designed to enhance…Mon, 05 Oct 2026 12:00:00 GMTAnna Bedford et al.Researchhttps://doi.org/10.14742/ajet.11584https://doi.org/10.14742/ajet.11584A reflective learning dashboard powered by generative artificial intelligence (AI) was developed to enhance self-regulated learning, student engagement, academic performance and learner perceptions in higher education. Grounded in learning analytics and generative feedback pedagogy, the system integrates behavioural…Mon, 05 Oct 2026 12:00:00 GMTThacha LawannaResearchhttps://doi.org/10.1016/j.caeai.2026.100686https://doi.org/10.1016/j.caeai.2026.100686This study examines the development of teachers’ AI-integrated teaching competence, defined as AI-Technological Pedagogical Content Knowledge (AI-TPACK), aiming to move beyond additive assumptions toward an ethically grounded model. Based on survey data from 454 Chinese university teachers, a suppression pattern…Mon, 05 Oct 2026 12:00:00 GMTXueyi CHEN et al.Researchhttps://doi.org/10.1007/s11423-026-10728-6https://doi.org/10.1007/s11423-026-10728-6Abstract This study examined the impact of learning analytics-based personalized feedback on student engagement behavior, learning outcomes, and perceptions of the feedback in a large introductory STEM course. Students were randomly assigned to either an experimental group, which received four personalized feedback…Mon, 05 Oct 2026 12:00:00 GMTJ. M. Russell et al.Researchhttps://doi.org/10.1007/s11423-026-10736-6https://doi.org/10.1007/s11423-026-10736-6Mon, 05 Oct 2026 12:00:00 GMTEsti Swatika Sari et al.Researchhttps://doi.org/10.1186/s41239-026-00626-5https://doi.org/10.1186/s41239-026-00626-5Abstract Understanding how humans process visual information during learning remains a central challenge in cognitive neuroscience. Traditional approaches typically rely on behavioral observations or indirect neural measures, which limits direct insight into learners' internal cognitive representations. Recent…Mon, 05 Oct 2026 12:00:00 GMTZhu Su et al.Researchhttps://jedm.educationaldatamining.org/index.php/index/article/view/1147https://jedm.educationaldatamining.org/index.php/index/article/view/1147Intelligent Language Tutoring Systems (ILTS) aim to provide adaptive language-learning experiences tailored to individual students. A major challenge is determining the appropriate granularity of Knowledge Components (KCs) for predicting student performance. Accurate prediction is essential for identifying activities…Mon, 05 Oct 2026 12:00:00 GMTWalid El Hefny and Detmar MeurersResearchhttps://doi.org/10.63608/ssj.4145https://doi.org/10.63608/ssj.4145First-year, first-time (FYFT) students enter university with expectations shaped by institutional recruitment communications, social media, and broader media narratives. During transition, lived experiences may diverge from these expectations, creating expectation-experience mismatch. While student expectations and…Mon, 05 Oct 2026 12:00:00 GMTCourtney Geritz et al.Researchhttps://arxiv.org/abs/2610.05579v1https://arxiv.org/abs/2610.05579v1Academic dishonesty is an urgent problem in computer science (CS) education, compounded by the rise of artificial intelligence (AI) and the prevalence of online learning in a post-COVID world. Self-efficacy and the impostor phenomenon are psychological constructs linked to academic dishonesty, yet little research has…Sun, 04 Oct 2026 22:29:56 GMTChandler C. PayneResearchhttps://arxiv.org/abs/2610.05301v1https://arxiv.org/abs/2610.05301v1Generative artificial intelligence (GenAI) is increasingly embedded in university students' academic work, yet student engagement is often examined through adoption, frequency of use, or general perceptions, with less attention to how it is shaped by linguistic and sociotechnical conditions. This comparative…Sun, 04 Oct 2026 15:25:17 GMTMing LiResearchhttps://doi.org/10.1111/bjet.70093https://doi.org/10.1111/bjet.70093Abstract Generative artificial intelligence (GAI) has become a prevalent tool in higher education, yet little is known about how its use affects the learning experiences of minority‐ethnic international students (MEIS). Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) and a four‐strand…Sun, 04 Oct 2026 12:00:00 GMTOlatunji David Adekoya et al.Researchhttps://doi.org/10.1111/bjet.70092https://doi.org/10.1111/bjet.70092Abstract Process‐oriented approaches are needed to understand the temporality and dynamics of teacher–student interactions in higher education classrooms. In this study, we follow a complexity science approach, based on non‐linear intra‐ and inter‐personal physiology methods, to explore the physiology of…Sat, 03 Oct 2026 12:00:00 GMTMuhterem Dindar et al.Researchhttps://arxiv.org/abs/2610.02720v1https://arxiv.org/abs/2610.02720v1Growing volumes of educational real-world data (ERWD) are being collected across learning platforms and institutional systems. Sharing these data within the Learning Analytics community offers substantial research opportunities, yet access remains limited by ethical, regulatory and governance constraints. Prior work…Fri, 02 Oct 2026 02:57:21 GMTHibiki ItoResearchhttps://arxiv.org/abs/2610.02444v1https://arxiv.org/abs/2610.02444v1Large language models often solve a theorem forward yet fail to disprove a closely related false one: a falsification gap that supervised fine-tuning does not close and can actively worsen. We frame counterexample generation as constrained witness emission against a deterministic per-theorem Python verifier, and…Thu, 01 Oct 2026 20:13:14 GMTOmar Farouk ZouakResearchhttps://doi.org/10.1016/j.compedu.2026.105757https://doi.org/10.1016/j.compedu.2026.105757Educational-technology histories are often organized around successive tools, obscuring how action, decision-making, regulation, and adaptive work are distributed among human and technological actors. Drawing on 5,387 papers published in Computers & Education over fifty years, this study examines how Agency and…Thu, 01 Oct 2026 12:00:00 GMTYuan‐Hsuan Lee et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105758https://doi.org/10.1016/j.compedu.2026.105758For the 50th anniversary of Computers & Education, this study describes the topic landscape and cross-period semantic connections among 5,226 articles published from 1980 to 18 June 2026. Independently fitted BERTopic models for 1980–2003, 2004–2013, 2014–2019, and 2020–2026 identified 11, 15, 18, and 9 non-noise…Thu, 01 Oct 2026 12:00:00 GMTXieling Chen et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105760https://doi.org/10.1016/j.compedu.2026.105760Research in educational technology has been extensively mapped in terms of what has been studied, but far less attention has been paid to how it has been studied. This paper reports a methodological review of fifty years of research in Computers & Education, covering a corpus of 5,516 articles published between 1976…Thu, 01 Oct 2026 12:00:00 GMTJo Tondeur et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105763https://doi.org/10.1016/j.compedu.2026.105763Thu, 01 Oct 2026 12:00:00 GMTJered Borup et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105759https://doi.org/10.1016/j.compedu.2026.105759Thu, 01 Oct 2026 12:00:00 GMTSara Monteyne et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105762https://doi.org/10.1016/j.compedu.2026.105762Technology-enhanced second language learning (TEL2L) has been rapidly expanding as a prominent research strand over the past five decades, yet systematic reviews focusing specifically on how theoretical frameworks connect technical affordances to L2 outcomes remain scarce. To address this gap, this study presents a…Thu, 01 Oct 2026 12:00:00 GMTRuofei Zhang et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105764https://doi.org/10.1016/j.compedu.2026.105764Thu, 01 Oct 2026 12:00:00 GMTAri KimResearchhttps://doi.org/10.1016/j.compedu.2026.105766https://doi.org/10.1016/j.compedu.2026.105766As the conversational AI agents become integrated into digital learning environments, students’ interaction with these systems can show conversational disengagement, including mocking, misleading, or otherwise diverting the agents. Although task-based disengagement such as gaming the system and off-task actions are…Thu, 01 Oct 2026 12:00:00 GMTJihyun Rho et al.Researchhttps://doi.org/10.1016/j.caeai.2026.100685https://doi.org/10.1016/j.caeai.2026.100685Background. Equipping learners with AI-ethical literacy is a priority for higher education, yet item-level self-reports leave two observables unrecorded: the structure of lexical co-use in learners’ written responses and the polarity of their free text. Neither is a measure of learning. A further inferential hazard is…Thu, 01 Oct 2026 12:00:00 GMTTengfei Shao et al.Researchhttps://doi.org/10.1016/j.caeai.2026.100687https://doi.org/10.1016/j.caeai.2026.100687AI anxiety is increasingly prevalent among university students, yet mainstream AI literacy interventions are often infeasible in resource-constrained settings. This study focuses on a law-dominant university in western China, where AI literacy education is absent. Students’ AI competence and AI ethical awareness are…Thu, 01 Oct 2026 12:00:00 GMTYaqiao Mu et al.Researchhttps://arxiv.org/abs/2610.01174v1https://arxiv.org/abs/2610.01174v1This research WIP paper presents DBWorkout, a web-based platform that supports formative SQL learning through sandbox-based execution, automated result-based feedback, and session-based gamification. Learning Structured Query Language (SQL) remains challenging for undergraduate students due to limited opportunities…Thu, 01 Oct 2026 06:47:24 GMTSehrish Basir NizamaniResearchhttps://arxiv.org/abs/2610.01158v1https://arxiv.org/abs/2610.01158v1This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective,…Thu, 01 Oct 2026 06:38:16 GMTSehrish Basir NizamaniResearchhttps://arxiv.org/abs/2610.01147v1https://arxiv.org/abs/2610.01147v1This innovative practice full paper examines a structured Virtual Global Collaboration (VGC) activity between undergraduate computing courses in the United States and Pakistan. The project was designed to support technical and intercultural skill development through asynchronous international teamwork in data…Thu, 01 Oct 2026 06:25:42 GMTSehrish Basir NizamaniResearchhttps://arxiv.org/abs/2610.00760v2https://arxiv.org/abs/2610.00760v2Large language models are increasingly used as collaborators on deductive-reasoning tasks, but their outputs can hallucinate or pull users away from intended reasoning. Formal proof assistants provide machine-checked verification, but have a steep learning curve and require more granular reasoning than human written…Wed, 30 Sep 2026 21:56:50 GMTChenjun GuoResearchhttps://doi.org/10.1016/j.caeo.2026.100426https://doi.org/10.1016/j.caeo.2026.100426Virtual Reality (VR) is increasingly integrated into university teaching, yet many VR projects lack clearly defined interdisciplinary roles. As a result, responsibilities remain unclear, collaboration becomes difficult, and technically sophisticated VR applications often fail to achieve their intended pedagogical…Wed, 30 Sep 2026 12:00:00 GMTNadine Bisswang et al.Researchhttps://doi.org/10.1007/s11423-026-10727-7https://doi.org/10.1007/s11423-026-10727-7Abstract This study considers the professional needs of instructional design (ID) and educational technology (ET) professionals, including not only the technical and domain-related knowledge and skills traditionally covered by most professional organizations and institutions, but also crucial professional and…Wed, 30 Sep 2026 12:00:00 GMTMarisa Exter and Iryna AshbyResearchhttps://doi.org/10.1080/00221546.2026.2739118https://doi.org/10.1080/00221546.2026.2739118The 2017 Tax Cuts and Jobs Act introduced a 1.4% excise tax on the net investment income of select nonprofit colleges. Affected colleges warned that the tax would force them to cut educational spending and reduce financial aid to students. Using a triple-difference design that exploits the policy’s dual eligibility…Wed, 30 Sep 2026 12:00:00 GMTYung­-Yu TsaiResearchhttps://arxiv.org/abs/2609.38552v1https://arxiv.org/abs/2609.38552v1Public institutions increasingly procure AI systems whose design they cannot inspect or change. In higher education, proprietary Early Warning Systems (EWS) leave colleges with few options beyond adjusting model outputs to address inequity. This raises the question of how fairness work is coordinated among vendors,…Tue, 29 Sep 2026 21:11:30 GMTKelly McConveyResearchhttps://arxiv.org/abs/2609.38029v1https://arxiv.org/abs/2609.38029v1Generative AI (GenAI) tools entered higher education classrooms faster than the field was able to study their effects on learning. One concern is that GenAI may displace the critical thinking and AI literacy that students will need after graduation. This paper reports a Design-Based Research pilot of a GenAI-assisted…Tue, 29 Sep 2026 17:08:08 GMTFatima Tuz ZahraResearchhttps://arxiv.org/abs/2610.00338v1https://arxiv.org/abs/2610.00338v1How students interact with artificial intelligence (AI) systems in educational settings may determine whether that interaction supports or displaces critical thinking. This paper introduces two contributions. The first is the Three Paths of Student-AI Interaction, a typological framework identifying three…Tue, 29 Sep 2026 15:26:54 GMTFatima T. ZahraResearchhttps://doi.org/10.1111/bjet.70089https://doi.org/10.1111/bjet.70089Abstract The rapid proliferation of generative artificial intelligence (GenAI) has made fostering learners' critical thinking an increasingly pressing educational challenge. While single‐agent GenAI applications are prone to limited perspective diversity and echo‐chamber effects, multi‐agent systems (MAS) offer a…Tue, 29 Sep 2026 12:00:00 GMTXiaoyan Chu et al.Researchhttps://doi.org/10.1007/s11423-026-10737-5https://doi.org/10.1007/s11423-026-10737-5Tue, 29 Sep 2026 12:00:00 GMTJiayao Zeng et al.Researchhttps://arxiv.org/abs/2609.37280v1https://arxiv.org/abs/2609.37280v1AI-assisted cheating has become an important threat to the security of online exams. This study examines whether the risk of AI-assisted cheating in the final exam can be predicted using students' digital traces in the learning management system (LMS) during the first eight weeks of the semester. The sample comprised…Tue, 29 Sep 2026 11:24:41 GMTGökhan AkçapınarResearchhttps://arxiv.org/abs/2609.36175v1https://arxiv.org/abs/2609.36175v1Tablet-based teaching and learning has become increasingly common in university mathematics courses, offering capabilities such as digital ink, dynamic visualisation, and real-time annotation. However, there is limited experiential evidence regarding how students perceive the specific benefits and drawbacks of…Mon, 28 Sep 2026 19:45:02 GMTDimitrios ChiotisResearchhttps://arxiv.org/abs/2609.36073v1https://arxiv.org/abs/2609.36073v1The rapid proliferation of large language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic integrity, especially in programming courses. We present Argus, an automated detection system for LLM-assisted student work in undergraduate C programming assignments.…Mon, 28 Sep 2026 18:22:55 GMTDavid RacovanResearchhttps://doi.org/10.1007/s11423-026-10734-8https://doi.org/10.1007/s11423-026-10734-8Mon, 28 Sep 2026 12:00:00 GMTPerihan Tekeli and Arif AltunResearchhttps://doi.org/10.1007/s11423-026-10732-whttps://doi.org/10.1007/s11423-026-10732-wAbstract Educational innovations should articulate a clear rationale that situates the innovation within existing literature. Current frameworks, such as ESSA Tier IV, emphasize this requirement, though they only mandate a general review of research. Consequently, the process for demonstrating this connection…Mon, 28 Sep 2026 12:00:00 GMTNatalia Ingebretsen Kucirkova et al.Researchhttps://doi.org/10.1080/00221546.2026.2735777https://doi.org/10.1080/00221546.2026.2735777In the ever-evolving landscape of intercollegiate athletics, postsecondary institutions are increasingly committed to creating inclusive environments that enable collegiate athletes to thrive academically. Some of the influential factors shaping athletes’ academic achievement may be their experiences of campus…Mon, 28 Sep 2026 12:00:00 GMTAgustín J. García et al.Researchhttps://doi.org/10.1080/00221546.2026.2728560https://doi.org/10.1080/00221546.2026.2728560Although research on autistic college student success is growing, less is known about those who navigate both autism and co-occurring disabilities during college. This mixed-methods study employs a rigorous proposition testing approach to address critical gaps in the literature. Drawing on national survey and…Mon, 28 Sep 2026 12:00:00 GMTKarly B. Ball et al.Researchhttps://arxiv.org/abs/2609.33750v1https://arxiv.org/abs/2609.33750v1Large Language Models (LLM) have become popular tools in education. Therefore, understanding how students interact with LLMs for learning purposes is an important aspect of the responsible design and development of LLM based learning tools. However, existing research has focused on evaluating model performance,…Sun, 27 Sep 2026 16:50:40 GMTOpetunde IbitoyeResearchhttps://arxiv.org/abs/2609.33720v1https://arxiv.org/abs/2609.33720v1Revision traces provide valuable evidence about students' writing processes, but their usefulness for learning analytics depends on how individual revisions are represented. Automated draft-comparison methods often produce granular edit operations that can fragment a single purposeful revision into multiple analytic…Sun, 27 Sep 2026 16:23:05 GMTYu TianResearchhttps://doi.org/10.1177/00915521261487956https://doi.org/10.1177/00915521261487956Objective: Timely and effective academic advising is critical to community college student success. As colleges engage in initiatives aimed at accelerating completion rates, many institutions are simultaneously working to implement new advising structures that bolster the success of these initiatives. Our goal was to…Sun, 27 Sep 2026 12:00:00 GMTGerald V. Bourdeau et al.Researchhttps://doi.org/10.1007/s11423-026-10725-9https://doi.org/10.1007/s11423-026-10725-9Sat, 26 Sep 2026 12:00:00 GMTKyung KimResearchhttps://doi.org/10.1016/j.caeo.2026.100425https://doi.org/10.1016/j.caeo.2026.100425Employers increasingly expect graduates to utilize large language models (LLMs) in the workplace, yet the competencies needed for computing roles across Africa remain unclear, given varying national contexts. This study examines how six LLMs, namely ChatGPT-4, DeepSeek-V2, Gemini 1.5 Pro, Claude 3.5 Sonnet, Llama 3…Fri, 25 Sep 2026 12:00:00 GMTPrecious Eze et al.Researchhttps://doi.org/10.1111/bjet.70088https://doi.org/10.1111/bjet.70088Abstract Cognitive presence and social presence are essential for computer‐supported collaborative learning (CSCL). Yet, monitoring and providing personalised feedback to support these presences remain a persistent challenge, particularly in the context of social annotation (SA). Generative Artificial Intelligence…Thu, 24 Sep 2026 12:00:00 GMTMengru Pan et al.Researchhttps://doi.org/10.1007/s11423-026-10723-xhttps://doi.org/10.1007/s11423-026-10723-xThu, 24 Sep 2026 12:00:00 GMTFang-ying Lo et al.Researchhttps://doi.org/10.1007/s11423-026-10713-zhttps://doi.org/10.1007/s11423-026-10713-zAbstract This study developed and validated a suite of Generative Artificial Intelligence Readiness and Perception (GenAI-RP) Scale for faculty and students in higher education. Three subscales, including a) Readiness, b) Benefit, and c) Challenge were created based on a review of relevant literature and validated…Thu, 24 Sep 2026 12:00:00 GMTStella Yun Kim et al.Researchhttps://doi.org/10.1007/s11423-026-10731-xhttps://doi.org/10.1007/s11423-026-10731-xThu, 24 Sep 2026 12:00:00 GMTFeng Lin et al.Researchhttps://doi.org/10.1080/00221546.2026.2735779https://doi.org/10.1080/00221546.2026.2735779Thu, 24 Sep 2026 12:00:00 GMTDion T. HarryResearchhttps://arxiv.org/abs/2609.28918v1https://arxiv.org/abs/2609.28918v1Students increasingly turn to AI for help with problem-solving, yet too much AI support can undermine learning itself. To benefit from AI, students need to specify the necessary knowledge and scaffold type in their questions. However, they struggle to formulate such targeted questions because they lack metacognitive…Thu, 24 Sep 2026 01:56:12 GMTHyoungwook JinResearchhttps://doi.org/10.1177/00915521261487945https://doi.org/10.1177/00915521261487945Objective: The purpose of this conceptual manuscript is to extend the results of a series of studies by examining an unexpected observation in our work in community college Career Technical Education (CTE) courses. We offer a multi-disciplinary theoretical critique that we believe has important implications for…Wed, 23 Sep 2026 12:00:00 GMTJAMES ROY KING et al.Researchhttps://doi.org/10.1177/00915521261488777https://doi.org/10.1177/00915521261488777Objective: Over one-third of community college students are aged 25 years or older (e.g., commonly categorized as “adult learners”), and there is a growing body of scholarship that makes sense of their college and career decisions through an individual as well as an institutional lens. Using asset-based frameworks,…Wed, 23 Sep 2026 12:00:00 GMTMarco A. Murillo et al.Researchhttps://doi.org/10.1016/j.caeai.2026.100682https://doi.org/10.1016/j.caeai.2026.100682Generative AI (GenAI) is increasingly adopted in design education, yet evaluating its educational value through final outcomes provides an incomplete picture. This study compares two ecologically plausible workflows in an architectural conceptual design task: GenAI-assisted image generation and ArchDaily-based…Wed, 23 Sep 2026 12:00:00 GMTYao Xiao et al.Researchhttps://doi.org/10.1016/j.caeo.2026.100422https://doi.org/10.1016/j.caeo.2026.100422This paper presents expert perspectives derived from a visioning activity conducted by an international panel of educational researchers from 13 countries and 18 institutions, working under Thematic Working Group 5 for UNESCO’s EDUsummIT 2025. Using a futures thinking approach, the experts co-constructed six scenarios…Wed, 23 Sep 2026 12:00:00 GMTJoyce Malyn‐Smith et al.Researchhttps://doi.org/10.1007/s11423-026-10703-1https://doi.org/10.1007/s11423-026-10703-1Abstract Although research indicates that Digital Personalised Learning (DPL) can improve aspects of early-years literacy and numeracy in low- and middle-income countries (LMICs), it is necessary to determine whether teacher-facing learning analytics dashboards further impact education outcomes. Specifically, there is…Wed, 23 Sep 2026 12:00:00 GMTChen Sun et al.Researchhttps://doi.org/10.1007/s11423-026-10707-xhttps://doi.org/10.1007/s11423-026-10707-xWed, 23 Sep 2026 12:00:00 GMTAleksandra Getman et al.Researchhttps://arxiv.org/abs/2609.27184v1https://arxiv.org/abs/2609.27184v1Generative artificial intelligence (GenAI) is increasingly described in higher education as a tool, collaborator, evaluator, proxy, and infrastructure. These labels are not neutral: they shape who is seen as acting, knowing, and, crucially, being answerable when someone uses AI. This study examines how scholars…Wed, 23 Sep 2026 00:43:34 GMTBiranchi PoudyalResearchhttps://doi.org/10.1111/bjet.70090https://doi.org/10.1111/bjet.70090Abstract Generative artificial intelligence (GenAI) has become a routine source of feedback, explanation, drafting support and task guidance in higher education. This study examined how individual students' self‐reported GenAI reliance and perceived human agency co‐varied over time in an AI‐supported collaborative…Tue, 22 Sep 2026 12:00:00 GMTYanyi Wu and Xinyu LuResearchhttps://doi.org/10.1111/bjet.70086https://doi.org/10.1111/bjet.70086Abstract Young children increasingly engage with digital media tools for purposes of education, entertainment and communication. This study examines children's digital media activities within the context of their larger home learning environment (HLE). Using data from two different samples ( N = 289), we looked at…Tue, 22 Sep 2026 12:00:00 GMTBurcu H. Tatar et al.Researchhttps://doi.org/10.1016/j.caeai.2026.100684https://doi.org/10.1016/j.caeai.2026.100684Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing learners to delegate explanation, writing, problem solving, and other cognitive work to systems that can generate, recommend, and sometimes act on the learner’s behalf. This…Tue, 22 Sep 2026 12:00:00 GMTLixiang Yan and Dragan GaševićResearchhttps://doi.org/10.1016/j.caeai.2026.100683https://doi.org/10.1016/j.caeai.2026.100683This study examines how generative AI can support middle school scientific inquiry in a rural agriculture-STEM classroom. Using a mixed-methods design with concurrent data collection and sequential analytic integration, we analyzed 42 eighth-grade students, organized into 12 groups, as they used Aspen, a custom AI…Tue, 22 Sep 2026 12:00:00 GMTSelçuk Kılınç et al.Researchhttps://doi.org/10.63608/ssj.4110https://doi.org/10.63608/ssj.4110First-semester performance is a practical indicator of students’ transition into tertiary education and a point at which institutions can intervene early. This study examined an analytic dataset of 2,048 incoming students admitted to 10 academic programs in the College of Business Administration at the University of…Tue, 22 Sep 2026 12:00:00 GMTJairo A. Ayala‐Godoy et al.Researchhttps://doi.org/10.1016/j.compedu.2026.105751https://doi.org/10.1016/j.compedu.2026.105751Mon, 21 Sep 2026 12:00:00 GMTChengchen Li et al.Researchhttps://arxiv.org/abs/2609.23309v1https://arxiv.org/abs/2609.23309v1Cross-expertise technical communication (i.e., communicating about computing topics across levels of computing expertise) is important for workplace collaboration. However, students are underprepared because computing education rarely explicitly teaches communication, and available practice tends to occur among peers…Sun, 20 Sep 2026 02:57:16 GMTJinyoung HurResearchhttps://arxiv.org/abs/2609.22549v1https://arxiv.org/abs/2609.22549v1AI is rapidly transforming higher education, including the application process, yet relatively little is known about its use and consequences. To help close this gap, we analyze nearly 7{,}500 applications submitted between 2020 and 2025 to a large public policy master's program in the United States. We find that in…Fri, 18 Sep 2026 20:06:56 GMTCalvin IsleyResearchhttps://doi.org/10.1016/j.caeai.2026.100680https://doi.org/10.1016/j.caeai.2026.100680Generative AI, currently most visible in education through large language models (LLMs), is increasingly embedded in students’ everyday learning practices, supporting tasks such as writing, coding, reasoning, and analysis. Yet their educational value remains uncertain because systems designed to improve immediate task…Fri, 18 Sep 2026 12:00:00 GMTHassan Khosravi et al.Researchhttps://doi.org/10.1016/j.caeai.2026.100681https://doi.org/10.1016/j.caeai.2026.100681While studies on using generative AI for education are increasing, research on whether it benefits STEM and non-STEM students, who tend to have different technology acceptance and AI literacy, in the interdisciplinary learning context is rare. To respond to this gap, this exploratory quasi-experiment study engaged 130…Fri, 18 Sep 2026 12:00:00 GMTGaoxia Zhu et al.Researchhttps://doi.org/10.1016/j.caeo.2026.100423https://doi.org/10.1016/j.caeo.2026.100423The integration of artificial intelligence (AI) in education (AIED) promises significant pedagogical transformations. However, it also raises concerns that have intensified global discourse on the ethical principles guiding its design and use. Despite this growing discourse, limited empirical evidence exists from the…Fri, 18 Sep 2026 12:00:00 GMTOwolabi Paul Adelana et al.Researchhttps://doi.org/10.1080/00221546.2026.2735778https://doi.org/10.1080/00221546.2026.2735778This quasi-experimental study examined the effectiveness of a one-time institutional microgrant program on academic and financial outcomes among Pell-eligible undergraduate students. Academic outcomes investigated included retention to the next semester, degree completion, attempted credit hours, and grade point…Fri, 18 Sep 2026 12:00:00 GMTBradley R. Curs et al.Researchhttps://doi.org/10.63608/ssj.4038https://doi.org/10.63608/ssj.4038As access to tertiary education has increased worldwide, the importance of understanding the complexities of student engagement has become progressively evident. Yet, studies on student engagement are frequently abstract, and commonly informed by statistics on completion rates. Understandings of student engagement at…Fri, 18 Sep 2026 12:00:00 GMTIrma Eloff et al.Researchhttps://arxiv.org/abs/2609.21600v1https://arxiv.org/abs/2609.21600v1Access to academic support is a key determinant of student success, yet students experience it unequally: some readily seek help from lecturers or tutors, while others hesitate due to anxiety, fear of judgement, uncertainty about expectations, or low confidence in their understanding. This may be especially evident in…Fri, 18 Sep 2026 10:30:26 GMTAndy GrayResearch