The increasing accessibility of Artificial Intelligence (AI), particularly Generative AI (GenAI) and chatbots, highlights the need for users to develop critical thinking skills to evaluate information and understand these systems. While critical thinking is essential for interacting with such technologies, current AI literacy efforts lack robust empirical validation, and effective educational methodologies have yet to be explored. This study explores the potential of the Socratic Method, a structured dialogue-based approach that fosters reflection and critical questioning, to support the development of critical thinking within a GenAI environment. Recognizing that many technology-enhanced and Socratic learning environments often overlook the role of learners’ domain expertise and prior knowledge in shaping effective questioning strategies, the study further compares within-domain and outside-domain Socratic dialogues to evaluate the pedagogical effectiveness of both conditions. Eighty participants, comprising higher education students and teachers, took part in workshops where they interacted with GenAI using two Socratic-style prompts, one within-domain and one outside-domain, and completed questionnaires assessing critical thinking, technical competence, and trust in technology after each interaction. Findings highlight the value of guiding GenAI interactions through Socratic reasoning within domain-relevant contexts to enhance perceived critical thinking, while also stressing the challenges of influencing user trust and perceived competence in technology.
Integrating Generative Artificial Intelligence (GenAI) into digital game-based learning (DGBL) environments presents new opportunities for enhancing student learning through personalised support, immediate feedback, and adaptive interaction. While previous research has explored the effectiveness of GenAI in various educational contexts, limited studies have examined how GenAI can support students’ self-regulated learning (SRL) within game-based science learning environments. Addressing this gap, the present study aimed to explore the impact of GenAI-supported self-regulated digital game-based learning (GenAI-SRDGBL) on junior high school students with different academic achievement levels, focusing on their learning motivation, behavioural patterns, perceptions, and prompt usage in a physics course. Using a quasi-experimental design, 48 students were categorised into high and low achievers based on their academic achievement. Data were collected through achievement tests, learning motivation questionnaires, in-game activity logs, student illustrations, and student-GenAI prompt interactions, and were analysed using statistical methods and Epistemic Network Analysis. High-achieving students reported higher post-intervention intrinsic motivation and used GenAI more strategically to seek advanced information and connect learning resources, whereas low-achieving students showed more repetitive GenAI access and relied heavily on basic information and formula prompts. The groups did not differ significantly in extrinsic motivation or illustrated perceptions. These findings highlight the need for differentiated, scaffolded GenAI support to foster effective self-regulation and to prevent metacognitive overreliance in game-based learning.
Video-Based Learning has become a central modality in digital education, with embedded Multiple-Choice Questions (MCQs) recognized as an effective strategy for promoting engagement and assessing comprehension. However, manually creating high-quality MCQs is challenging for instructors. This study presents a framework that leverages Large Language Models (LLM) to automate MCQ generation from video transcripts while ensuring alignment with established item-writing criteria. Unlike most prior approaches, the framework explicitly integrates the Item-Writing Flaws (IWF) checklist into the generation prompt, rather than using it solely for post-hoc evaluation. It segments video transcripts into topics, extracts and classifies key concepts, and applies structured prompting strategies to generate MCQs adhering to the IWF checklist and additional criteria from the literature, including relevance, difficulty, contextual specificity, and answerability. The framework was evaluated through a mixed-methods study involving seven instructors from diverse disciplines who assessed 257 generated MCQs. The results indicate that the majority of items met established quality guidelines. Specifically, 84.05
Whereas constructivist pedagogy promotes the use of interactive learning materials for students to make sense of new concepts, some concepts are difficult to concretize, and consequently educators might rely instead on axiomatic procedural rules. Such compromised educational design most affects students with disabilities. We maintain that any mathematical concept can be rendered accessible for all students. Here we examine the case study of basic arithmetic with positive and negative numbers, which often confuses students due to these numbers’ non-intuitive ontological constitution (e.g., What ‘thing’ is “− 3”?) and procedural complexity (e.g., What does “2– − 3” mean?). Leveraging the Special Education Embodied Design framework (SpEED), we developed an inclusive, technologically enabled educational activity centered on navigating the number line. 30 middle-school students, both with cognitive disabilities (n=9) and without (n=21) participated individually in a 45 min evaluation trial of our design. Audio–video data, demographics, teacher testimonials, and assessment scores were gathered. While participants with disabilities scored significantly lower than their non-disabled peers on an integer arithmetic pre-test, exploratory patterns suggest no evidence of a difference in achievement levels on the post-test. Microgenetic case studies indicate conceptual advantages enjoyed by participants in both groups who successfully coordinated egocentric proximal actions (walking a floor-based number line) with perception of distal semiotic enactment (of an avatar mirror-walking their action on its own number line). Enacting dynamically instantiated mathematical concepts, it appears, may offer benefits across learning profiles. If corroborated through future research, SpEED could bear broadly across instructional contexts and concepts.
With the rapid integration of generative artificial intelligence into educational environments, the motivational impact of Large Language Models (LLMs), such as ChatGPT, has become a critical area of inquiry. While LLMs are often promoted for their ability to personalize learning and increase engagement, empirical findings on their actual influence on student motivation remain fragmented. To address this gap, the present study conducts a meta-analysis of 51 experimental and quasi-experimental studies published between 2022 and 2025, systematically investigating the effects of LLM-supported instruction on student learning motivation. The results reveal that: (1) LLMs have a moderate and statistically significant positive effect on student motivation (SMD = 0.48); (2) LLMs more strongly enhance intrinsic motivation (e.g., curiosity, mastery goals) than extrinsic motivation (e.g., external incentives); (3) The greatest gains were observed in the “to accomplish” dimension of intrinsic motivation, reflecting improvements in goal-directed effort and perceived competence; (4) LLMs had limited or even negative effects on integrated regulation, indicating a potential misalignment between AI-generated content and students’ internalized personal values; (5) Moderator analyses showed that motivational outcomes varied significantly depending on the type of LLM integration, educational level, discipline, and intervention duration; (6) Short-term interventions and applications in humanities and social sciences demonstrated stronger positive effects on motivation, although one study involving younger learners reported comparatively large motivational gains, the current evidence base remains insufficient to draw generalized conclusions for this population (k = 1), highlighting an important gap for future research.The results provide timely, evidence-based guidance for educators, developers, and policymakers seeking to responsibly and effectively implement LLMs in educational settings.
This study examined how the use of gestures and embodiment influences the computational thinking (CT) skills and programming self-efficacy during unplugged debugging activities. A 2 × 2 factorial experimental design was employed, manipulating gesture type (congruent vs. incongruent) and embodiment type (direct vs. surrogate) during activities. Seventy-seven grade 2–3 students were randomly assigned and participated in debugging tasks, using either congruent gestures (aligning codes with movement) or incongruent gestures (arranging codes in a linear sequence), followed by direct embodiment (physically moving a character) or surrogate embodiment (observing a researcher move the character). Computational thinking was assessed using graphic-based measures of sequence comprehension, pattern recognition, and debugging, as well as transfer to text-based programming tasks. Data were analyzed using ANCOVA and ANOVA models, and findings revealed that incongruent gestures enhanced graphic-based CT proficiency, while surrogate embodiment improved the transfer to text-based programming, particularly in pattern recognition. Additionally, a significant interaction effect indicated that congruent gestures with direct embodiment produced the highest programming self-efficacy. These findings highlight the critical role of action-based learning in computational thinking, supporting unplugged activities as effective learning tools and reinforcing the need for inclusive curricula that enhance CT proficiency and programming self-efficacy in young learners. These findings suggest that different embodied debugging strategies uniquely support understanding, knowledge transfer, and efficacy, showing the importance of strategic integration of action-based activities in young learners.
Computer-supported collaborative argumentation (CSCA) creates valuable learning opportunities for students to explore multiple perspectives, co-construct knowledge, and develop argumentation skills. Grounded in collaborative learning and argumentation theories, we designed a CSCA activity to engage students in critical discussions of a controversial issue, with the goal of fostering perspective-taking, evidence-based reasoning, and critical evaluation of arguments. This activity included a series of structured tasks targeting key argumentation skills and gave student opportunities to explore both sides of the issue. We conducted a study with 64 middle school students to examine their dialogic patterns and argumentation skills in this activity. Results revealed a wide range of participation in the critical discussion across groups and individual students. Most collaborative efforts involved coordinating task responsibilities and processes among team members. Students’ argumentative discourse focused on developing and refining reasons and inviting teammates to provide ideas and elaboration. Interestingly, we found a negative correlation between the number of student dialogic turns and their scores on the collaborative argument task. This relationship can be explained by the differences across grade levels: older students performed better on the task despite engaging in fewer dialogic turns compared to younger students. By the end of their critical discussion, most groups proposed a team solution that integrated both sides of the issue. We conclude with implications for designing CSCA activities and support to promote middle school students’ argumentation skills.
The shift to online and blended learning, accelerated by the COVID-19 pandemic and the rise of generative AI tools, has intensified the search for valid and secure assessment methods. Oral assessments have long been recognized for their ability to authentically measure understanding, foster deeper engagement, and develop professional communication skills; however, logistical demands often limit their use. This paper reports findings from two studies investigating Asynchronous Oral Assessments (AOAs) delivered through a web-based platform and implemented alongside in-person, multiple-choice examinations. Study 1, conducted in an intermediate accounting course, explored associations between AOA participation and performance on multiple-choice exam items, revealing positive trends though not statistically significant. Study 2, conducted in a data analytics course, compared student performance across AOAs and in-person multiple-choice exams and found higher scores on AOAs, along with moderate correlations between formats. Survey results further indicated that students prepared differently for AOAs, reported greater use of active study strategies, and perceived the assessments as professionally relevant and cognitively engaging. Collectively, results suggest that AOAs represent an administratively scalable and pedagogically meaningful complement to traditional assessments.
This study analyzes linguistic patterns in reflective writing among graduate students in an instructional design course, using the Best Possible Self (BPS) method. The BPS exercise encourages students to envision their ideal professional selves, supporting early-stage professional identity development. We used Linguistic Inquiry and Word Count (LIWC) to examine psychological processes, emotional tone, social orientation, and temporal focus in students’ narratives. The analysis identified three reflective profiles—Causal Reflection-focused Individualists, Goal-Driven Pragmatists, and Social Collaborators—representing different approaches to identity development within a single course context. These profiles reflect emergent reflective patterns, not generalizable learner types. To assess whether professional identity development differed by cluster, we compared pre- and post-course survey responses. Results showed that Social Collaborators demonstrated significantly greater gains in professional identity scores compared to the other groups. While these findings are based on a short-term, context-specific study and do not imply causality, they suggest that socially oriented reflection may support identity growth. By integrating linguistic and narrative analysis, the study highlights diverse student approaches to constructing a professional identity. The results emphasize the value of adapting instructional strategies to support varied reflective orientations.
Although generative AI is increasingly integrated into K–12 education, prior research has emphasized post-intervention outcomes rather than how students interact with AI or how post-intervention competencies support human–AI collaboration. This mixed-methods study examined phase-based changes in students’ AI interactions, pre–post changes in AI dispositions, prompt engineering skills, and human–AI collaboration competencies, and predictors of post-intervention collaboration competencies. Sixty-nine eighth-grade students participated in a five-day STEM–AI curriculum using ChatGPT. Data from student-generated prompts, pre–post surveys, and competency tests were analyzed through content analysis, repeated-measures MANOVA, and multiple regression analyses. Results indicated that students’ AI interactions evolved from exploratory use toward argumentation and metacognitive monitoring. Students showed significant improvements in AI dispositions, prompt engineering skills, and human–AI collaboration competencies. Ethical awareness, particularly accountability and privacy, emerged as a significant predictor of post-intervention collaboration competencies. These findings suggest that generative AI can support higher-order thinking as a collaborative partner and that the development of human–AI collaboration competencies depends more on ethical awareness than on prompt engineering skills alone.
This study examines the use of ChatGPT as a design-support tool for developing career promotion content. In the digital transformation of society, areas such as career guidance and promotion are increasingly being reshaped by the speed, scalability, and accessibility of artificial intelligence (AI). Specifically, the study investigates how ChatGPT, a generative AI tool, can function as a design support system in the development of career promotion video content by generating structured interview scripts, profession-specific scenarios, and narrative components. The aim of the study is to identify the strengths and limitations of the ChatGPT-supported content development process, to evaluate the appropriateness of the content according to the feedback from professionals and students, and provide practical implications for educational content development and career guidance. Within the scope of the study, career promotion videos were developed for 38 different professions using three formats: interviews with professionals, a day in the profession scenarios, and critical decision moments. Data collection tools included researcher process notes, expert evaluation forms completed by professionals, and student feedback surveys following video implementation. Descriptive and thematic analyses were used to analyze the data. The findings indicate that ChatGPT-supported content development was perceived to enhance efficiency, scalability, and structured content generation, while human review and expert validation remained essential for ensuring contextual accuracy and professional authenticity. Overall, the findings suggest that conversational generative AI tools such as ChatGPT can serve as valuable design-support systems for educational video content development workflows, particularly in generating structured career narratives and scalable multimedia content. The study provides empirical and design-oriented insights into the integration of generative AI into educational content development and career promotion content development.
As programming education gains global importance, learners often struggle due to the cognitive demands, particularly in textual environments. Visual programming tools have been developed to ease this burden, but learners’ cognitive abilities—specifically executive function (EF)—may still affect their programming process and strategies. This study investigates how visual scaffolding influences the programming strategies of learners with different EF levels. Thirty-two adult participants were randomly divided into experimental and control groups, using programming systems with and without visual scaffolding respectively. Their EF was assessed using the Simon and running span tasks. Also, an eye-tracker was used to explore their cognitive processes during programming tasks and to identify their programming strategies. Results show that high-EF learners tended to adopt top-down strategies regardless of scaffolding. In contrast, low-EF learners without scaffolding used mixed strategies, while those with visual scaffolding were guided toward adopting a top-down approach. These findings highlight the role of visual scaffolding in supporting low-EF learners by fostering more effective programming strategies and mitigating cognitive differences between high- and low-EF learners.
In this study, I examine The Great Zimbabwe (2021) board game as a pedagogical tool for teaching African archaeology and recent history through tabletop play. As digital and analog games increasingly shape 21st-century storytelling, their capacity to represent, reinterpret, and commodify the past demands critical analysis. Through analysis of the game’s design, mechanics, artwork, and player experience, I explore how The Great Zimbabwe constructs, represent, and transmit knowledge about ancient African civilizations. The findings indicate that the game embeds rich historical references within an economy-driven gameplay model. Players manage craftsmen, worship deities, trade cattle, and build monuments, mirroring the economic interdependence, religious pluralism, and political complexity that characterized ancient polities such as Great Zimbabwe, Mapungubwe, Mutapa, Kilwa, Lozi, and Zulu. Unlike most Eurocentric civilization board games that emphasize self-sufficiency, The Great Zimbabwe rewards cooperation, reflecting Africa’s historical trade networks and reciprocal political economies. Pedagogically, the game enables experiential learning by allowing players to simulate decision-making, economic trade, and spiritual life, transforming abstract history into lived experience. However, the study also highlights limitations inherent in Western-designed board games. The game compresses diverse African cultures, religions, languages, and histories into a homogenized fantasy, obscuring gender, kinship, and artistic nuances. Ultimately, the game occupies a productive but ambivalent space between history and imagination, serving both as a valuable educational tool and a reminder of the interpretive boundaries of play-based pedagogy.
School vacations disrupt learning continuity and often erode the momentum students need to stay engaged across time; more recently school closures due to the pandemic have produced similar outcomes. When students are not in school for an extended period, breaks in continuity and waning momentum often translate into irregular engagement with learning activities, allowing previously learned skills to grow rusty. The Keep in School Shape (KiSS) Program repurposes online survey software to provide students with convenient and engaging daily reminders and opportunities to maintain the math skills they need for their future studies via text message or email during times when school is not in session. This paper articulates the architectural logic of a low-stakes, voluntary review environment through a bounded design case and advances a set of transferable design commitments that can inform the design of similar interventions.
This study explores the interactions between the quality of ICT access, ICT self-efficacy, math attitude, and math achievement utilizing the 2022 Programme for International Student Assessment (PISA) data for Hong Kong, Finland, and Türkiye. The study sample comprises 5190 participants from Hong Kong, 10,239 from Finland, and 7250 from Türkiye. The analyses based on structural equation modeling demonstrate that in all three countries, the quality of access to ICT significantly and positively affects both ICT self-efficacy and math attitude, which in turn affects math achievement. On the other hand, the direct effect of the quality of access to ICT is negative and significant on math achievement. Additionally, the economic, social, and cultural status (ESCS) plays a notable role in shaping ICT self-efficacy, math attitude (except Türkiye), and math achievement. ICT self-efficacy and math attitude mediate the relationship between ICT access and math achievement. The results underscore the importance of ICT access in enhancing students’ ICT self-efficacy and math attitudes and ultimately contributing to improved math achievement.
Ethics is recognized as a foundational element of human decision-making and a critical competency across educational disciplines, especially in STEM, where solving authentic, complex problems is essential. Traditional methods of ethics instruction often fail to provide the engagement needed for learners to grasp abstract ethical challenges. In contrast, game-based learning offers an experiential approach capable of supporting ethical literacy. Yet, despite their potential, games may also hinder learning or even promote unethical behavior, highlighting a critical but often overlooked challenge: balancing engaging gameplay with the meaningful integration of ethics-related objectives. Given the growing popularity and accessibility of analogue games in ethics education, this study explores their potential through four business ethics board games, each featuring distinct ethical goals, mechanics, and themes. A four-day workshop was conducted with 32 adult participants from diverse cultural, professional, and age backgrounds, with structured interviews following each gameplay session. By thematically analyzing participants’ experiences and perceptions, the study aims to deepen understanding of the complex interplay between games and ethics learning. Adopting a dialectical perspective, the research critically explores both the benefits and challenges of game-based approaches across different stages of the learning process. As a result, the pedagogical dilemmas related to rules, social modes, identity framing, systems nudging, personalized design, and ethics positioning were identified and discussed, spanning personal, behavioral, and environmental dimensions. Building on these insights, recommendations are proposed for designing and implementing games in ethics education, addressing aspects such as facilitation, instructions, goal settings, and social dynamics, thereby supporting meaningful, ethically informed learning experiences.
As generative artificial intelligence (AI) becomes increasingly embedded in higher education, a key educational question is not simply whether AI is used in design classrooms, but how it is instructionally integrated. This study examines how three instructional configurations in university design education—traditional instruction, a chatbot-assisted condition, and a fully integrated artificial intelligence-generated content (AIGC) condition—are associated with students’ AI-supported design product performance, self-efficacy, and role clarity / reverse-coded role ambiguity. Grounded in social cognitive theory and a role ambiguity perspective, the study conceptualizes AI use not as a binary presence-or-absence variable or a single technological feature, but as a set of instructional configurations that differ in AI availability, functional affordances, workflow integration, and pedagogical support. A quasi-experimental design was implemented in an undergraduate book design course at a comprehensive university in Hunan, China, involving 100 visual communication design students. Quantitative data were collected through pre- and post-intervention measures of design-related performance and self-efficacy, a post-intervention measure of role clarity / reverse-coded role ambiguity, and explanatory semi-structured interviews with a subset of students. The results showed that the fully integrated AIGC condition was associated with significantly higher AI-supported design product performance than the traditional and chatbot-assisted conditions, as well as the highest level of role clarity, corresponding to the lowest level of role ambiguity. However, because generative AI was available during the design process, this outcome should be interpreted as product-based task performance under AI-supported conditions rather than as direct evidence of independent learning gains. Traditional instruction and the fully integrated AIGC condition did not differ significantly in posttest self-efficacy, but both were associated with more favorable self-efficacy outcomes than the chatbot-assisted condition. The qualitative findings further suggested that these differences were related to continuity of workflow support, students’ sense of task control, and the clarity of human–AI role boundaries. The study contributes to research on AI-supported design education by comparing pedagogically meaningful instructional configurations, examining design product performance together with cognitive-affective perceptions, and providing ecologically relevant evidence from an authentic university classroom. The findings suggest that the educational value of AI in design education depends less on tool availability itself than on how AI is pedagogically embedded in design tasks, feedback processes, workflow structures, and human–AI role relationships.
As artificial intelligence (AI) becomes increasingly embedded in educational technologies and instructional practices, education programs are expanding offerings focused on AI in education (AIED). However, limited empirical research has examined how these programs conceptualize AI-related content, justify curricular decisions, and enact pedagogy. This qualitative study investigates how AIED programs conceptualize AI literacy, determine curricular priorities, and design instructional experiences for educators. Semi-structured interviews were conducted with faculty members responsible for designing and teaching AIED courses across six higher education institutions. Data were analyzed using thematic analysis informed by AI literacy and AI-TPACK frameworks. Findings indicate that programs consistently emphasize foundational AI literacy, ethical awareness, and critical evaluation of AI tools. Faculty intentionally prioritize interpretive and pedagogical understanding over technical proficiency, reflecting concerns about accessibility, limited instructional time, and teachers’ diverse computational backgrounds. However, attention to subject-specific AI integration was uneven, with relatively few programs explicitly addressing how AI aligns with disciplinary epistemologies and instructional goals. Additionally, educators’ positionality toward AI, such as openness, skepticism, and ethical concern, emerged as an important yet underdeveloped dimension of teacher AI literacy. Pedagogically, programs commonly employ constructivist approaches, including project-based learning, collaborative learning, and reflective practice, to support educators’ sense-making of AI technologies. This study contributes to the literature by illuminating how AIED programs navigate tensions between general AI literacy and discipline-specific integration, and by highlighting the need to more explicitly address AI positionality and subject-specific pedagogy in AIED. Implications are discussed for the design of AIED curricula and for future research on AIED.
Teacher educators face significant challenges in preparing candidates for the complexities of modern classrooms, particularly regarding inclusive pedagogy. Despite attempts to enhance multicultural diversity in teacher education, gaps remain in supporting students, combating bullying, and integrating inclusive curricula. This study explores the effects of an online simulation designed to equip teacher candidates to address these challenges in elementary settings. Grounded in situated (Lave Wenger, 1991) and simulation-based learning (Levin Frei-Landau, 2023), the simulation offers a safe environment for candidates to practice decision-making and reflect on their biases. Findings from qualitative data, including pre- and post-surveys of teacher candidates (TCs) and interviews with teacher educators (TEs), indicate a notable increase in TCs’ confidence and self-efficacy in inclusivity and multicultural engagement. TCs moved from understanding abstract concepts to actionable strategies, but noted potential backlash from parents. TEs, on the other hand, recognized the simulation’s effectiveness in fostering critical thinking and empathetic responses, but noted challenges in curriculum integration. The study emphasizes the potential of simulations as vital tools in bridging the gap between theory and practice in teacher preparation while identifying areas for further research.
As librarianship has evolved, it is important for librarians to align themselves with other stakeholders (administrators, teachers) within the K-12 ecosystem. However, studies that compare alignment across different stakeholders are often limited. Based on this gap, this study surveyed a sample (N = 186) of K-12 administrators, educators, and librarians to understand differing perceptions of school librarians’ roles as defined by the Future Ready Schools—Librarian Framework. Results found no statistically significant difference in perceptions among administrators, teachers, and librarians on curriculum, instruction assessment; community partnerships; collaborative leaderships; data and privacy. However, differences emerged on some constructs (e.g.—use of space and time; literacy; personalized professional learning; robust infrastructure; budget and resources). Implications for research and practice are discussed.
