A Multidimensional Conceptual Framework of Generative AI As a Co-Instructor: Measurement Formulation for Personalized and Sustainable Higher Education

Transformative Roles of Generative Artificial Intelligence as a Co-Instructor: A PRISMA-Guided Systematic Literature Review for Personalized and Sustainable Higher Education

A synthesized conceptual perspective is contributes a synthesized conceptual perspective that integrates pedagogical, ethical, governance, and sustainability dimensions into a unified framework for responsible AI-augmented higher education.

Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning

A holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning is proposed, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills.

RECONFIGURING INSTRUCTIONAL COHERENCE: A FUZZY DELPHI VALIDATION OF A MULTI-DIMENSIONAL DESIGN FOR BIG IDEAS–ORIENTED EFL INSTRUCTION

Big Ideas have been introduced as a principle for organizing curriculum knowledge to support conceptual coherence in EFL. Existing alignment-based models, which focus on coordinating objectives, teaching activities, and assessment, remain limited when instructional design involves multiple interacting dimensions. This study reconceptualizes instructional coherence as a structurally differentiated condition and develops a multi-dimensional framework for EFL instructional design. Using the Fuzzy Delphi Method, 10 experts evaluated 37 design items across five dimensions: objectives, content, instructional strategies, resources, and assessment. All items met the acceptance criteria (d ≤ 0.20; consensus ≥ 75%). The results reveal a patterned distribution of agreement, distinguishing stable core elements from context-sensitive components. Objectives, content, and assessment show relatively high stability, while strategies, resources, and operational aspects of assessment require contextual adaptation. Based on these findings, the study proposes a structured design framework that supports instructional planning by stabilizing key elements while allowing flexibility in implementation. The framework provides practical guidance for EFL teachers in organizing coherent instruction under varying classroom conditions.

Adaptive Learning Systems in Saudi Higher Education: A Contextualised Conceptual Framework for Student Engagement

Purpose – This paper develops a theoretical framework linking adaptive learning system (ALS) characteristics to student engagement in Saudi higher education, and specifies how two structural features of Saudi public universities, gender-segregated campuses and an instructor-guided pedagogical tradition, shape that relationship. Design/methodology/approach – This is a theory-adaptation article. Literature published between 2018 and 2025 was synthesized thematically through constructivist learning theory, self-determination theory, and cognitive load theory to build and refine testable propositions, not to quantify effect sizes or produce a census of the field. Findings – The paper proposes an integrated framework and states seven propositions. Gender segregation and instructor-guided tradition are specified as moderators of the ALS to engagement relationship rather than as barriers, and Arabic language processing quality is specified as a boundary condition. A qualitative gap analysis identifies where the evidence base constrains implementation decisions. Practical implications – A phased implementation sequence and a course level decision checklist translate the framework into institutional and classroom practice, with the first phase designed as an empirical test of the framework’s central mediation claim rather than as a rollout. Originality/value – The paper reframes two features of Saudi higher education usually treated as demographic controls or generic barriers into specified, falsifiable moderators, and states the resulting propositions explicitly so that they can be confirmed or refuted by future empirical work.

Developing a substantive grounded theory of generative artificial intelligence (GenAI) integration in science research instruction

An Integrated Human and Digital Communication Model for AI-Empowered Translation Education

This study proposes an Integrated Human and Digital Communication (IHDC) model that conceptualizes translation learning as a communicative and cognitive process shaped by dynamic interactions between human agents and AI systems. Drawing on the Transactional Model of Human Communication, the Community of Inquiry framework, digital literacy, Bloom’s revised taxonomy, and critical thinking pedagogy, the model integrates pedagogical, social, and cognitive dimensions into a unified framework. It advances a three-mechanism structure, namely cognitive inquiry, communicative interaction, and pedagogical guidance. Translation competence is developed via structured human–AI engagement under teacher guidance. Critical thinking is operationalized through a question-based engine derived from Bloom’s taxonomy. A preliminary expert evaluation with five domain specialists assessed the model across five dimensions, yielding initial support for its conceptual coherence, with particularly strong ratings for utility, understandability, and acceptability. By extending communication theory to include AI as an active participant, the IHDC model offers a theoretically grounded and operational framework for integrating human and digital communication in translation education. Further empirical research is needed to examine its effectiveness in classroom contexts.