Jul 2026· International Research Journal of Computer Science· Vol 13, pp. 613· 0 citations· 6 references
TL;DR
It is suggested that effective teacher preparation must transcend content delivery and focus on equipping teachers with adaptive, student-centred methodologies grounded in cognitive science.
Abstract
This paper explores the preparation of teachers through the lens of modern educational and learning theories, with particular attention to how artificial intelligence (AI) can augment educational planning and professional teacher development. Three major theoretical frameworks are examined: (1) Constructivist Theory, rooted in Piaget's cognitive development model, emphasizing the learner's active role in knowledge construction; (2) Meaningful Learning Theory (Ausubel), which underscores the critical link between new knowledge and pre-existing cognitive structures; and (3) Information Organization and Processing Theory, which explains how memory encoding and retrieval affect learning outcomes. The paper further integrates the McCarthy 4MAT model of learning styles to illustrate how AI-driven, learner centred strategies can be operationalized in contemporary teacher preparation programmers. Findings suggest that effective teacher preparation must transcend content delivery and focus on equipping teachers with adaptive, student-centred methodologies grounded in cognitive science.
: The growing use of generative artificial intelligence in Comprehensive English instruction has been accompanied by a tendency to treat AI-assisted preparation, drafting, and revision as evidence of improved learning. This paper examines that assumption by asking how AI-supported materials can be incorporated into a course whose central work remains close reading, language practice, and the formation of students’ own understanding. Drawing on constructivist learning theory and distributed cognition, the paper proposes a teacher-student-AI coordination model for interactive-generative teaching. The model identifies three conditions under which AI support can become pedagogically meaningful. Teachers need to make course goals and tool-use boundaries explicit through task design. Students need to explain and revise their understanding in ways that make meaning construction visible. The intelligent system needs to preserve questions, drafts, feedback, and revision traces for later judgment. From this perspective, AI-assisted learning in Comprehensive English shows practical value in extending resources, supporting feedback, and recording the learning process, yet its value depends on how these functions are brought back into text-based tasks, classroom interaction, and teacher assessment. These distinctions help clarify the responsibilities of teachers, students, and intelligent systems in AI-mediated language learning and provide a course-level pathway for redesigning content, interaction, process, and assessment in Comprehensive English.
Sha Li, Yuheng Sun· Frontiers in Educational Res...· 0 citations
This embedded case study examined how eight in-service teachers from rural and under-resourced districts engaged with mathematical creativity (MC), content knowledge (CK), and pedagogical content knowledge (PCK) during an AI-guided professional development program, and how specific AI-mediated mechanisms shaped that engagement. Teachers completed ten interactive modules in which a generative AI system served as a cognitive and instructional partner by prompting problem solving, problem posing, representational reasoning, simulated student interpretation, and reflective instructional decision-making. Data sources included teacher-AI dialogue logs, teacher-generated mathematical artifacts, and interviews, which were analyzed through iterative thematic analysis. Findings showed that teachers engaged with MC, CK, and PCK as interconnected forms of reasoning rather than as isolated domains. Three cross-case themes characterized this engagement: creative mathematical exploration, conceptual deepening of proportional reasoning, and expansion of pedagogical reasoning. These trajectories were shaped by six core AI-mediated mechanisms: adaptive and personalized prompting, real-time feedback, progressive scaffold fading, simulated student reasoning, conversational nonjudgmental tone, and flexible pacing. Two cross-cutting mechanisms, representational nudges and cycles of creative challenge and reflection, further supported teachers’ movement between mathematical exploration, conceptual reasoning, and pedagogical decision-making. Teachers emphasized that these mechanisms enabled productive struggle within a psychologically safe environment and positioned AI as a thinking partner rather than a content-delivery tool. The study contributes to research on AI-supported teacher learning by showing how mathematical creativity-aligned AI scaffolding can support teachers’ integrated engagement with mathematics and pedagogy in rural professional learning contexts where access to sustained professional development is limited.
Ali Bicer, Tugce Aldemir, Unggi Lee et al.· ZDM: Mathematics Education· 1 citation
Techno-pedagogical competency is commonly discussed as a teacher's capacity to combine technological, pedagogical, and content knowledge, yet this formulation says relatively little about the cognitive work through which prospective teachers learn to make defensible technology-integration decisions. This critical narrative review examines that developmental problem by synthesising research on technological pedagogical content knowledge (TPACK), metacognition, self-regulated learning, professional reasoning, cognitive load, transfer, motivational beliefs, contextual knowledge, and emerging artificial-intelligence-mediated lesson design. The review searched accessible education and multidisciplinary scholarly sources for work published from 1 January 2005 to 27 June 2026, while retaining selected earlier foundational studies needed to interpret cognition and teacher knowledge. Evidence indicates that techno-pedagogical competency is better understood as an integrated, situated capability than as the accumulation of separate technical skills. Development appears to depend on repeated cycles of pedagogical problem representation, activation and coordination of content-pedagogy-technology knowledge, evaluation of technological affordances and constraints, design justification, metacognitive monitoring, feedback-based revision, enactment, and transfer. Direct evidence is strongest for structured metacognitive support, subject-specific TPACK modules, design-based learning, authentic technology experiences, and reflective feedback, although intervention studies remain comparatively few. Self-efficacy, technology-related beliefs, institutional support, field experience, subject matter, and contextual knowledge influence whether cognitive resources are translated into instructional action. Major limitations in the literature include heavy reliance on self-report measures, short interventions, cross-sectional mediation models, weak measurement of real-time reasoning, limited observation of classroom enactment, and scarce evidence linking prospective teachers' competency development with sustained practice or pupil learning. Recent generative artificial intelligence research extends the cognitive agenda by making prompt formulation, epistemic verification, ownership, and agency visible as new components of pedagogical reasoning. The review proposes a cognitive-developmental account in which competent technology integration emerges from calibrated knowledge, regulated reasoning, contextual adaptation, and accountable professional judgement rather than from tool proficiency alone.
G. Swathika, G. Rajeswari· Asian Journal of Education a...· 0 citations
: The entry of generative artificial intelligence into college English classrooms has changed the way students obtain resources, revise their language output, and participate in classroom interaction. While existing studies have mainly emphasized tool functions and teaching efficiency, less attention has been paid to how learning motivation is supported and weakened in this new instructional structure. Drawing on Self-Determination Theory, this paper examines learning motivation in generative AI-supported college English classrooms from the perspective of three-way interaction among the teacher, the student, and the intelligent system. It argues that AI can support motivation when system suggestions create opportunities for autonomous judgment, feedback and revision help students confirm their competence, and human-machine practice returns to authentic classroom communication. At the same time, algorithmic dependence, instant answers, and technological mediation may weaken autonomy, reduce the experience of competence, and undermine relatedness. The paper further proposes practical adjustments from both teacher support and student autonomy, emphasizing that system output should become learning material only after pedagogical judgment, critical processing, and interpersonal verification. This analysis clarifies the motivational conditions and risk boundaries of AI-supported college English teaching.
Sha Li, Yuheng Sun· Advances in Educational Tech...· 0 citations
GenAI is positioned as a transformative force in language education that offers technological innovation and new frameworks for inclusive, responsive, and emotionally attuned instruction.
Abderahman Rejeb, Karim Rejeb, Heba F. Zaher et al.· Quality & Quantity· 0 citations
It is argued 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.
