Purpose This study re-examines the relevance of the ADDIE instructional design model in the context of Generation Alpha, learners born into a world shaped by artificial intelligence (AI) and immersive technologies. It aims to propose a future-oriented adaptation of ADDIE that addresses the digital fluency, interactivity and personalisation expectations of this emerging generation, contributing to open, distance and lifelong learning contexts. Design/methodology/approach This study employs a content-driven and thematic literature review to synthesise conceptual insights on instructional design, human-computer integration (HInt) and explainable artificial intelligence (XAI). To support conceptual resonance with digitally fluent learners, brief exploratory reflections were gathered from 20 Generation Z undergraduates. These reflections were used as illustrative alignment inputs to inform refinement of the proposed model rather than as empirical validation. The paper further examines how each phase of the ADDIE framework: analysis, design, development, implementation and evaluation, can be enhanced through interactive, gamified and transparent technologies, aligning instructional design with the evolving needs of AI-mediated learning environments. Findings The study suggests that HInt may support learner autonomy and multimodal engagement, while XAI is positioned to strengthen trust and transparency in instructional processes. Exploratory student reflections indicate conceptual alignment between FLEX-ADDIE's design principles and digitally fluent learner expectations, reinforcing its positioning as a forward-looking conceptual framework for inclusive, interpretable and adaptive instructional design in online and open learning. Research limitations/implications This study adopts a conceptually driven design research approach supported by exploratory reflections from 20 Generation Z learners, used as illustrative alignment inputs rather than empirical validation. As such, the scope is limited to conceptual development and does not aim for generalisability. Future research should prioritise empirical evaluation of FLEX-ADDIE across diverse institutional and cultural contexts using experimental, quasi-experimental or longitudinal designs. Foundational studies may examine learner trust, interpretability comprehension and measurable indicators of human-AI co-agency. This study contributes by advancing a theoretically grounded instructional framework that integrates explainable AI and human-centred integration within AI-mediated open and distance learning (ODL) environments. Practical implications FLEX-ADDIE offers actionable guidance for ODL institutions to design AI-mediated learning environments that are transparent, adaptive and learner-centred. By integrating HInt and XAI across all ADDIE phases, the framework supports real-time feedback, personalised learning pathways and interpretable analytics. Institutions can apply FLEX-ADDIE to enhance instructional design, strengthen learner trust and improve engagement through multimodal and AI-supported interactions. Implementation may involve AI-capable infrastructure, faculty readiness and governance frameworks addressing data ethics and transparency. The model provides a structured yet flexible approach for integrating AI into instructional design practice. Social implications FLEX-ADDIE promotes more equitable and responsible AI use in education by emphasising transparency, interpretability and human-AI co-agency. By integrating explainable AI into instructional design, the framework supports informed learner participation and reduces risks associated with opaque algorithmic decision-making. In ODL contexts, it enables more inclusive and accessible learning experiences for digitally diverse populations. The model also encourages ethical governance practices, helping institutions address concerns related to data privacy, bias and accountability. Overall, FLEX-ADDIE contributes to building trustworthy AI-mediated learning ecosystems that support learner autonomy and social responsibility. Originality/value This paper introduces FLEX-ADDIE, a conceptual adaptation of the ADDIE framework that integrates HInt and XAI to address emerging demands in AI-mediated education. It offers a proactive instructional design approach for anticipating the needs of Generation Alpha within ODL contexts. The model's originality lies not in introducing AI tools but in systematically embedding human-AI co-agency and explainable mechanisms across all ADDIE phases. FLEX-ADDIE advances a structured, transparent and adaptive design framework, providing a foundation for future empirical validation and practical implementation.
Nurdayana Mohamad Noor (Thu,) studied this question.