Future-Proof Learning: Redesigning ADDIE for Generation Alpha with Explainable AI and Human-Computer Integration
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| Title: | Future-Proof Learning: Redesigning ADDIE for Generation Alpha with Explainable AI and Human-Computer Integration |
|---|---|
| Language: | English |
| Authors: | Nurdayana Mohamad Noor (ORCID |
| Source: | Asian Association of Open Universities Journal. 2026 21(1):34-47. |
| Availability: | Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Generational Differences, Age Groups, Instructional Design, Models, Undergraduate Students, Student Attitudes, Technology Uses in Education, Man Machine Systems, Technology Integration |
| DOI: | 10.1108/AAOUJ-07-2025-0091 |
| ISSN: | 2414-6994 |
| Abstract: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1508311 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1508311 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Future-Proof Learning: Redesigning ADDIE for Generation Alpha with Explainable AI and Human-Computer Integration – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nurdayana+Mohamad+Noor%22">Nurdayana Mohamad Noor</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6797-3830">0000-0002-6797-3830</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Asian+Association+of+Open+Universities+Journal%22"><i>Asian Association of Open Universities Journal</i></searchLink>. 2026 21(1):34-47. – Name: Avail Label: Availability Group: Avail Data: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 14 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Generational+Differences%22">Generational Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Groups%22">Age Groups</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Man+Machine+Systems%22">Man Machine Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1108/AAOUJ-07-2025-0091 – Name: ISSN Label: ISSN Group: ISSN Data: 2414-6994 – Name: Abstract Label: Abstract Group: Ab Data: 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. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1508311 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1108/AAOUJ-07-2025-0091 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 34 Subjects: – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Generational Differences Type: general – SubjectFull: Age Groups Type: general – SubjectFull: Instructional Design Type: general – SubjectFull: Models Type: general – SubjectFull: Undergraduate Students Type: general – SubjectFull: Student Attitudes Type: general – SubjectFull: Technology Uses in Education Type: general – SubjectFull: Man Machine Systems Type: general – SubjectFull: Technology Integration Type: general Titles: – TitleFull: Future-Proof Learning: Redesigning ADDIE for Generation Alpha with Explainable AI and Human-Computer Integration Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nurdayana Mohamad Noor IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 2414-6994 Numbering: – Type: volume Value: 21 – Type: issue Value: 1 Titles: – TitleFull: Asian Association of Open Universities Journal Type: main |
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