Intelligent tutoring systems (ITSs) are increasingly used to support personalized learning through adaptive instruction, content, and feedback. However, we suspect that the existing meta‑system design of ITSs emphasizes learner‑oriented components, such as the Domain Model, the Learner Model, and Tutor Models, while underrepresenting educators’ perspectives in real‑world learning environments. This study explores how educator perspectives appear in empirical reports of implemented ITSs in real-world learning environments and how these perspectives align with the existing meta‑system-design of ITSs. Following PRISMA 2020 guidelines, we conducted a systematic literature review across seven major academic databases and identified 1,068 publications from 2014 to 2024. Relevant studies for a full‑text analysis were selected (n = 21) to form a database of text artifacts describing educators’ perspectives on ITSs and educator-ITS teaming situations implemented in real-world learning environments. In this way, 435 text artifacts were extracted using five defined analytical lenses. These text artifacts were further analyzed using an epistemological network analysis and synthesized through a directed content analysis. The findings highlight mismatches between reported educational practices and the existing meta‑system design of ITSs. Drawing on these insights, we propose an extension of the meta‑system design that incorporates educators’ perspectives on ITSs and educator-ITS teaming situations, grounded in the principles of Learning Design and the Teaching Cycle. The illustrated enhancement aims to inform interdisciplinary research and the EdTech sector in designing and developing ITSs from a holistic perspective on smart learning environments, to strengthen explainability, usability, and transparency for educators.
This research develops an educational platform utilizing a design-thinking model to enhance computational thinking and digital literacy among undergraduate students in Thailand. The study was conducted in three phases: (1) investigating the learning experiences and needs of 400 learners and instructors; (2) designing and developing the integrated design-thinking model to improve computational thinking; and (3) deploying the platform and design-thinking learning model to enhance student computational thinking using 28 undergraduate students, evaluating its effectiveness and learner satisfaction. Quantitative analysis revealed that the platform significantly improved computational thinking mean scores at the 0.05 level, suggesting its potential as an effective learning tool. The prototype learning innovation comprises four key components: (1) fostering computational skills and digital literacy, with an emphasis on logical and algorithmic thinking, data handling, and software usage; (2) online assistance through the learning platform “Code Craft,” providing support and resources; (3) digital and artificial intelligence tools, facilitating interactive, modern learning experiences; and (4) enjoyment, ensuring an engaging and motivating learning process. The steps in this process are as follows: (1) discovering insights into learners’ needs and challenges; (2) tailoring the platform to user requirements; (3) generating ideas, encouraging creativity and innovative thinking; (4) prototyping solutions, developing practical applications and tools; and (5) testing and iteration, refining the platform from feedback and performance data. This approach illustrates the effectiveness of combining design-thinking instructional models and gamification in educational platform development, significantly improving computational thinking, preparing students for the labor market, and addressing the skills gap in technology and computational skills in higher education.
The widespread digital technologies, including artificial intelligence, have largely elevated student experiences and achievements in higher education, but the simultaneous upgrade of teacher capacity, particularly the resources and opportunities for teachers’ professional development in everyday class, were less attended. To investigate how a smart classroom can be orchestrated to boost teachers’ self-exploration and collaboration in digital teaching, this study employed the technological pedagogical content knowledge (TPACK) framework to probe how teachers advanced their professional learning through design, implementation, and revision of VoiceThread microlectures for students’ Chinese character learning outside the class. Collaborative action research was conducted over four years to create a teaching community comprising the researcher and five teachers to enhance contextual smartness, that is, digital technologies and teachers’ context-aware, adaptive, and personalized use of them, in Chinese language classrooms. Semi-structured teacher interviews, community discussion, and surveys from 227 students showed that teachers utilized microlecturing to strengthen their TPACK about Chinese characters alongside class progress. This TPACKing process not just promoted research-practice dialogues but reinforced teachers’ beliefs and strategies to carry out professional learning in smart classrooms. The findings suggested a dynamic re-framing of TPACK with means of updating and interacting with contexts, content, technologies, and pedagogies in smart learning environments. The study also offered tips to keep up language teachers’ continuing professional development in daily instruction.
The integration of artificial intelligence (AI) into education has raised important questions about its implications for students’ analytical reasoning and independent judgment. ChatGPT, a generative AI model capable of producing human-like responses, is widely used in academic contexts, yet its educational value requires careful evaluation. This paper presents and evaluates EduPartnerAI, a platform that integrates ChatGPT into student assignments and is designed to encourage critical engagement with AI-generated content. The platform records student-AI interactions, requires students to reflect on ChatGPT’s outputs, and provides teachers with tools to review observable indicators of student reasoning and AI response quality. An exploratory study involving 61 undergraduate students was conducted, relying on student reflection questionnaires, a usability survey, teacher evaluations, and an analysis of recorded interactions. Students generally perceived ChatGPT as a useful learning tool that provided clear explanations and practical examples, although some recognized that its responses could be overly general or incomplete. The teacher observed that students asked relevant questions and effectively incorporated AI-generated content into their assignments but experienced some difficulties with error detection, independent judgment, and critical evaluation. The teacher also judged ChatGPT’s outputs to be largely relevant, clear, and accurate, with few instances of bias or misleading information. Overall, the findings provide preliminary insights into student and teacher perspectives and observable patterns of engagement, highlighting both the educational potential of generative AI and the need for appropriate instructional scaffolding.
Little is known about students’ epistemic agency in relation to AI-mediated practices. This study was conducted in a Swedish school that had started the pilot project “AI on the schedule” for compulsory school students, for whom the national curricula do not include AI training. A survey covering the aspects of source evaluation, data privacy, algorithmic manipulation, transparency, responsibility, and governance was distributed to these year 7 students (n = 47). The data was analysed through cross-tabulations of question pairs. This revealed how epistemic agency varied across aspects within individual students. Results show that students demonstrated strong epistemic agency in certain areas but weak agency in others, for example, in data privacy and algorithmic manipulation, even when these had been part of earlier instruction. The contribution of the study is that epistemic agency in AI-mediated practices is not a unified construct: the data suggest distinct output-directed and system-directed dimensions, each with its own area of application. This has implications for instruction and guidance when integrating AI into K-12 teaching.
