Jul 2026· ETDC: Indonesian Journal of Research and Educational Review· 0 citations· 45 references
TL;DR
One of the first comprehensive syntheses of AI integration across the entire CAR cycle is offered, linking it explicitly to critical-thinking development within a reflective, teacher-led inquiry framework, an intersection that remains underexplored in the extant literature.
Abstract
The rapid development of Artificial Intelligence (AI) in education has opened new opportunities for pedagogical innovation, including in the practice of Classroom Action Research (CAR). This study aims to analyze and synthesize how AI is integrated across the CAR cycle and to examine its contribution to the development of students' critical thinking skills. A systematic literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. Twenty peer-reviewed articles published between 2023 and 2026, retrieved from Google Scholar, ScienceDirect, and ERIC, were analyzed through thematic categorization and narrative synthesis. AI is no longer positioned merely as an instructional tool; it is increasingly embedded across all stages of the CAR cycle, with the most prominent role emerging in the observation and reflection phases, where it supports learning data analysis and delivers rapid, adaptive feedback. AI integration is associated with improved student engagement and learning outcomes, as well as stronger analytical, evaluative, and argumentation skills. However, the benefits are not automatic: unstructured or excessive use of AI risks fostering dependency and reducing the depth of students' independent thinking. This review offers one of the first comprehensive syntheses of AI integration across the entire CAR cycle—planning, acting, observing, and reflecting—linking it explicitly to critical-thinking development within a reflective, teacher-led inquiry framework, an intersection that remains underexplored in the extant literature.
Artificial Intelligence (AI) has rapidly evolved from a specialized computational technology into an increasingly important component of higher education, influencing teaching, learning, assessment and academic support. Intelligent tutoring systems, adaptive learning platforms, learning analytics, automated feedback, conversational agents and generative AI are creating new possibilities for personalized and interactive learning. At the same time, the rapid integration of AI has raised important concerns regarding student engagement, critical thinking, academic independence, AI literacy, teacher competence, digital infrastructure and responsible use. Existing research provides evidence of considerable educational potential, but findings remain fragmented across AI technologies, pedagogical approaches, disciplines and learning outcomes. This review critically examines the emerging literature on AI-based pedagogy in higher education, with particular emphasis on its relationship with student engagement and critical thinking. It further investigates the roles of AI literacy, perceived usefulness, teacher competence, AI-related technological-pedagogical knowledge, digital infrastructure and institutional readiness in determining the effectiveness of AI-supported learning. The review is conceptually grounded in Artificial Intelligence in Education (AIED), the Technological Pedagogical Content Knowledge (TPACK) framework, AI literacy and technology-adoption perspectives. The conceptual focus is consistent with the original manuscript, which identified AI literacy, perceived usefulness and digital infrastructure as important antecedents of AI-based pedagogy and positioned student engagement and critical thinking as major educational outcomes. A structured literature-review methodology is adopted to identify, screen, organize and thematically synthesize relevant research, with particular attention to AI-enabled personalization, adaptive learning, intelligent tutoring, generative AI, student engagement, critical thinking, AI literacy and institutional readiness. The review distinguishes between technological interaction and meaningful cognitive engagement, recognizing that frequent AI use does not necessarily indicate deeper learning. Similarly, AI may support critical thinking when used for questioning, comparison, evaluation and reflection, but may weaken independent reasoning when students simply delegate cognitive tasks to AI. The review proposes an AI–Pedagogy–Engagement–Critical Thinking (AI-PECT) framework in which AI capabilities influence educational outcomes through pedagogical mediation and learner agency, while AI literacy, teacher competence, infrastructure, assessment design and ethical governance operate as important contextual conditions. The review argues that the educational value of AI depends less on the presence of AI technology itself and more on how deliberately, critically and responsibly it is integrated into teaching and learning. The findings provide a foundation for educators, institutions and policymakers seeking to move from simple AI adoption toward meaningful, human-centred and intellectually responsible learning.
Sonam Bansal and Vijay Kumar Lamba· International Journal of Adv...· 0 citations
The rapid advancement of artificial intelligence has made AI-based instruction, including intelligent tutoring systems, learning analytics dashboards, adaptive learning platforms, and generative AI, increasingly prevalent in K-12 education. However, AI-generated information does not automatically lead to pedagogical action; it gains instructional meaning only when teachers interpret, judge, and translate it into situated classroom support. Following PRISMA guidelines, this systematic review analyzed 29 peer-reviewed English-language studies published between 2016 and 2025 across six databases to examine how teacher intervention is constituted in K-12 AI-based instruction and what conditions shape its reported effects. The synthesis was organized around three dimensions: process, strategy, and effect. Findings indicate that teacher intervention operates as a cyclical process of monitoring, judgment, intervention, and orchestration, through which AI-generated information is transformed into pedagogical action and subsequent classroom adjustment. Three broad intervention strategies were identified: pedagogical translation of AI outputs, design of learning support, and reconstruction of interaction structures. Reported benefits for student learning and teacher orchestration were generally promising but conditional, varying according to the interpretability of AI information, intervention timing, target level, teachers’ implementation feasibility, and students’ autonomy. By presenting an integrated conditional framework, this review argues that the teacher’s role in AI-based instruction is being reconfigured rather than diminished, offering implications for teacher professional development and the design of teacher-facing AI systems.
The integration of Artificial Intelligence (AI) within Technical-Vocational Education and Training (TVET) is increasingly shaping the educational landscape, presenting both novel pedagogical opportunities and complex challenges. This systematic literature review investigates how instructors and students perceive and experience the implementation of AI-supported teaching and learning approaches in TVET settings. This review addresses the growing need to understand the human dimensions of AI adoption in vocational education, particularly from the perspectives of those directly involved in teaching and learning. As AI technologies become increasingly embedded in educational practices, examining the experiences, perceptions, and concerns of instructors and students is essential for informing effective and responsible implementation strategies. By synthesizing evidence across diverse TVET contexts, this review provides insights into emerging opportunities, challenges, and implications for policy and practice. Following the PRISMA 2020 framework, an initial database search on Lens.org yielded 580 records. After applying rigorous inclusion and exclusion criteria, including filters for publication date, document type, and subject matter, 19 empirical studies were selected for final qualitative synthesis. The findings reveal three overarching themes. First, while instructors exhibit optimism regarding efficiency gains, they express significant concerns about dehumanization, cognitive decline, and threats to academic integrity. Second, students experience enhanced psychological safety through instant feedback, yet face a paradox of being "Engaged but Amotivated" alongside risks of severe technological dependency. Third, AI implementation in TVET is uniquely constrained by the necessity for tactile skill mastery, industry precision, and alignment with local cultural practices. Ultimately, maximizing AI's potential in vocational education requires addressing systemic infrastructure barriers and preserving the hands-on, human-centric core of TVET.
Iron G. Morales, John Hillard Mansueto, Russel M. Dela Torre· International journal of res...· 0 citations
Design Thinking is conceptually broad and is often treated as a process, a mindset, a method, or a pedagogical framework, with no clear distinctions among them. This systematic review investigates whether Design Thinking (DT) is associated with learning experiences in higher education. The review examines how DT strategies are integrated into instructional practices and their influence on student engagement, creativity, academic satisfaction, and learning outcomes. Drawing on evidence from 27 empirical studies, it shows Design Thinking has been implemented through project-based learning, design studio pedagogy, flipped learning, STEAM integration, and culturally relevant methods. The review finds that Design Thinking-based instruction was reported to support student motivation, collaboration, problem-solving, creativity, critical thinking, and ownership of their learning. The review recommends further research into DT’s effectiveness using a clear measurement model, contextual variations of DT, comparative Design Thinking with non-DT active learning strategies, and longitudinal effects on student learning.
George Attah Aboagye, Kwame Fordjour Owusu, Emmanuel Aklasu et al.· Discover Education· 0 citations
This scoping review examines how generative artificial intelligence (GenAI) tools such as ChatGPT are used in educational contexts and how their use influences students’ critical thinking. A systematic search of four databases (Scopus, Web of Science, ERIC, and IEEE Xplore), followed by screening and eligibility assessment, identified 25 empirical studies published between 2022 and 2025. The findings show that GenAI supports critical thinking when embedded in structured learning activities that require students to question, evaluate, and revise AI-generated content. Tasks such as debate, error detection, and guided writing revision consistently promote analytical reasoning and reflection. In contrast, unstructured use often leads to passive acceptance of AI outputs and reduced cognitive engagement. To account for this divergence, the review proposes the Task–Mediation–Internalisation (TMI) model, which conceptualises AI as a mediational stimulus within a Vygotskian developmental process. Within this system, critical thinking emerges through socially mediated interaction and potentially becomes internalised through repeated meaningful engagement. The findings highlight the importance of designing AI-supported learning environments that position AI outputs as objects of reasoning rather than sources of answers.
Critical thinking is an essential competency for addressing the challenges of contemporary education. The objective of this systematic review was to analyze the teaching strategies with the strongest scientific support for its development across different educational contexts. The study was conducted according to the PRISMA 2020 guidelines through the review of twenty studies published between 2022 and 2026, selected from specialized databases according to quality and relevance criteria. The results show that problem-based learning, project-based learning, metacognition, and active methodologies are the most effective strategies for strengthening analysis, argumentation, problem-solving, and self-regulation skills. Likewise, generative artificial intelligence emerges as a complementary resource whose impact depends on appropriate pedagogical mediation. It is concluded that the development of critical thinking requires a comprehensive educational approach that articulates didactic innovation, teacher training, and emerging technologies.
Violeta Chamaya Becerra, Juan Pedro Soplapuco Montalvo· Minerva· 0 citations