Digital games are increasingly used as tools in science education, yet the growing literature remains distributed across research methods, game design and implementation, pedagogical approaches, and different forms of learning evidence. An integrated synthesis is therefore needed to clarify how the digital game-enhanced science education (DGSE) evidence base has developed and is structured. This study presents a systematic review of 63 peer-reviewed empirical studies on DGSE published from 2013 to 2023 and reported in accordance with PRISMA 2020. The findings revealed that: (1) DGSE research expanded substantially after 2018 but remained concentrated in school-age learners and general science contexts, with limited attention to preschool, higher education, teachers, and several discipline-specific areas; (2) medium-sized studies, quasi-experimental designs, and quantitative approaches formed the methodological center of the field, while mixed methods represented a substantial but smaller strand; (3) game implementation was concentrated in web-based and integrated environments and role-playing formats, whereas pedagogical design was more diverse and layered, combining contextualized/situated and inquiry/problem-centered frameworks with collaborative, reflective, scaffolding, narrative, and inquiry-sequencing supports; and (4) learning evidence extended from achievement and conceptual understanding to higher-order competencies, practical performance, motivation, and other learning processes, assessed through tests, self-reports, artifacts, qualitative evidence, behavioral traces, and multimodal data. By integrating these dimensions within a common analytic framework, the review provides a more coherent account of how DGSE research has been conducted, pedagogically organized, and evidenced, while identifying priorities for a more balanced and cumulative research base.
Students increasingly rely on online video for help with class material. Here we ask if this form of self-directed learning can be leveraged to improve learning outcomes of regular classroom instruction. Specifically, we test whether encouraging students to find relevant instructional videos online, paired with scaffolding through association questions, benefits learning in an introductory college physics course. A total of 103 undergraduate students were assigned to either a control group (conventional reading assignments and exercises) or an intervention group (unassisted free video search and viewing along with association questions). We also assessed working memory capacity, motivation, and collected their grade-point averages (GPA) to determine the effect of these individual traits. Learning performance was assessed via quizzes, exams, and pre/post physics knowledge tests. In all three measures, we found evidence for a beneficial effect of this intervention. However, on the exam the evidence was only indirect, namely, performance depended on the level of engagement with the association questions. Working memory, GPA, and motivation were significant predictors on most outcomes measures. These findings suggest that student engagement and cognitive traits play a critical role in the effectiveness of video-based, autonomous learning interventions. These results indicate that integrating scaffolding questions into regular at home self directed video search enhances learning, as students are prompted to actively engage with the material.
Collaborative Online International Learning (COIL) has emerged as a transformative pedagogical framework to foster intercultural competence and global engagement without the need to circumvent the logistical and socioeconomic barriers of physical presence. However, there is limited understanding of how heterogeneous institutional contexts, varying in terms of governance, resources, and disciplinary norms, affect program efficacy. This study presents a multi-year longitudinal analysis of four cohorts (2022–2025) within a transatlantic COIL initiative titled “Building transatlantic bridges in basic and applied research in Human Reproduction” across University of Aveiro (Portugal), São Paulo State University (Brazil), and Padre Albino University Centre (Brazil) (N = 222). We used a dual-phase evaluative framework comprising immediate post-intervention questionnaires (N = 85) and long-term alumni career tracking to assess skill acquisition, professional performance, and international networking. Participant satisfaction remained consistently high and “Intercultural Connection” and “Collaborative Synergy” (44.2
Student questioning is fundamental to physics learning, yet traditional intelligent tutoring systems often constrain this inquiry by following rigid instructional protocols. This study investigates how a Conversational Intelligent Tutoring Systems (CITS) based on a Large Language Model (LLM) transforms student engagement in thermodynamics education. Eighteen undergraduate students interacted with the CITS, generating 112 spontaneous questions that were analysed using a dual framework of metacognitive regulation and dialogic uptake. Our qualitative analysis characterises how students moved between passive content consumption and active knowledge construction through their own questioning. The findings show that students’ questions were not merely informational but were distinct acts of metacognitive regulation (planning, monitoring and evaluation). Furthermore, the vast majority of these student-initiated dialogues were of high quality, demonstrating deep cognitive engagement (Level 2 and 3 uptake). Critically, sustained Monitoring, in which students continuously tracked and tested their understanding during problem-solving, was significantly associated with high-level dialogic interaction and accounted for most instances in which students extended concepts to new contexts (Applying and Connecting). These findings suggest LLM-based systems can restore learner agency in physics education by creating conversational affordances that support the spontaneous, critical inquiry essential for deep conceptual understanding.
Hands-on practical work is widely treated as the epistemic core of school and university science. Artificial intelligence (AI) can now produce laboratory reports, answer practical questions, grade student work, and replace physical experiments with adaptive simulation, so the products of practical work can be obtained without the embodied activity that gives them value. This systematic review asks whether AI-era technologies threaten the manipulative, tacit, and procedural competencies that practical work is meant to build. We integrate cognitive offloading, the tacit dimension of learning, and the deskilling dynamic into a conceptual model, derive five propositions, and use them as an a priori coding framework. Following PRISMA 2020, we screened 3,897 unique records and synthesised 20 empirical studies (2019 to 2026) through best-fit framework synthesis, appraised with the Mixed Methods Appraisal Tool. Coding confirmed the two theorised displacement families, cognitive offloading and embodied substitution, resolved into five mechanisms led by simulation substitution (10 studies) and answer provision (9 studies). The central proposition, cumulative skill erosion, remains untested: no study measured practical competence experimentally, so the deskilling thesis rests on anecdotal and perception data. We contribute an operationalised model that separates task-level offloading from cumulative deskilling, an assessment of the evidence graded by the strength of the study designs that produced it, and a measurement agenda.
