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| Format: | Recurso digital |
| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20309267 |
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| _version_ | 1866901746162335744 |
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| author | Liew, Sharon Yu Choy |
| author_facet | Liew, Sharon Yu Choy |
| contents | <p><strong>Overview</strong><br>This repository archives the theoretical foundations, structural logic, and empirical methodology of the Conceptual Readiness of Artificial Intelligence Model (CRAM Framework). Designed specifically for the Technical and Vocational Education and Training (TVET) sector in Malaysia, CRAM operationalizes how psychological resilience, concern trajectories, and longitudinal career phases dictate an educator's readiness to integrate Artificial Intelligence into their pedagogy.</p> <p><strong>Theoretical Foundations & Framework Structure</strong><br>The CRAM Framework advances traditional educational adoption models by transforming them into a non-linear developmental trajectory. The architecture maps three primary dimensions of educator concern against change readiness:<br>• Self-Concerns: Evaluating the psychological barriers and cognitive bandwidth limitations of educators regarding professional security and personal competence.<br>• Task-Concerns: Investigating operational barriers, administrative weights, and logistical challenges of AI software management.<br>• Impact-Concerns: Analyzing the shift of educators into active instructional innovators focused on collaboration and student outcomes.</p> <p><strong>Regulating Mechanisms & Intervening Dynamics</strong><br>Unlike binary adoption models, CRAM introduces internal and contextual filters that regulate the transition from initial anxiety to strategic digital integration:<br>1. Cognitive Resilience Buffers: Internal psychological indicators that act as an emotional filter, transforming operational obstacles into mastery experiences and self-efficacy.<br>2. Professional Life-Cycle Filters: Contextual career-stage parameters that analyze the varying vulnerabilities of early-career educators against the deep-seated pedagogical paradigms of late-career academic veterans.</p> <p><strong>Methodological Architecture & Operationalizat</strong>ion<br>To validate the model's structural integrity, this framework establishes a robust, Four-Block Hierarchical Layered Regression Strategy:<br>• Block 1: Baseline Demographic Controls and baseline technology utilization frequencies.<br>• Block 2: Direct predictive paths of primary core concern dimensions.<br>• Block 3: Main effects of internal regulating variables and contextual professional filters.<br>• Block 4: Cross-product interaction terms assessing changes in the strength or direction of the relationship between predictors and overall readiness.</p> <p>The empirical design is operationalized through verified psychometric scales measuring concerns, growth trajectories, and career-stage classifications.</p> <p>Note on Intellectual Property Rights<br>This represents original conceptual framework development conducted as part of a doctoral study at Universiti Tun Abdul Razak (UNIRAZAK). It is recorded and published here as a restricted-access preprint to secure an immutable digital timestamp, establish prior art, and protect the candidate's unique intellectual property rights ahead of formal thesis defense and journal submission.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20309267 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | THE TECHNICAL ARCHITECTURE OF THE CRAM FRAMEWORK: MODERATING EDUCATOR READINESS FOR AI INTEGRATION IN MALAYSIAN TVET Liew, Sharon Yu Choy <p><strong>Overview</strong><br>This repository archives the theoretical foundations, structural logic, and empirical methodology of the Conceptual Readiness of Artificial Intelligence Model (CRAM Framework). Designed specifically for the Technical and Vocational Education and Training (TVET) sector in Malaysia, CRAM operationalizes how psychological resilience, concern trajectories, and longitudinal career phases dictate an educator's readiness to integrate Artificial Intelligence into their pedagogy.</p> <p><strong>Theoretical Foundations & Framework Structure</strong><br>The CRAM Framework advances traditional educational adoption models by transforming them into a non-linear developmental trajectory. The architecture maps three primary dimensions of educator concern against change readiness:<br>• Self-Concerns: Evaluating the psychological barriers and cognitive bandwidth limitations of educators regarding professional security and personal competence.<br>• Task-Concerns: Investigating operational barriers, administrative weights, and logistical challenges of AI software management.<br>• Impact-Concerns: Analyzing the shift of educators into active instructional innovators focused on collaboration and student outcomes.</p> <p><strong>Regulating Mechanisms & Intervening Dynamics</strong><br>Unlike binary adoption models, CRAM introduces internal and contextual filters that regulate the transition from initial anxiety to strategic digital integration:<br>1. Cognitive Resilience Buffers: Internal psychological indicators that act as an emotional filter, transforming operational obstacles into mastery experiences and self-efficacy.<br>2. Professional Life-Cycle Filters: Contextual career-stage parameters that analyze the varying vulnerabilities of early-career educators against the deep-seated pedagogical paradigms of late-career academic veterans.</p> <p><strong>Methodological Architecture & Operationalizat</strong>ion<br>To validate the model's structural integrity, this framework establishes a robust, Four-Block Hierarchical Layered Regression Strategy:<br>• Block 1: Baseline Demographic Controls and baseline technology utilization frequencies.<br>• Block 2: Direct predictive paths of primary core concern dimensions.<br>• Block 3: Main effects of internal regulating variables and contextual professional filters.<br>• Block 4: Cross-product interaction terms assessing changes in the strength or direction of the relationship between predictors and overall readiness.</p> <p>The empirical design is operationalized through verified psychometric scales measuring concerns, growth trajectories, and career-stage classifications.</p> <p>Note on Intellectual Property Rights<br>This represents original conceptual framework development conducted as part of a doctoral study at Universiti Tun Abdul Razak (UNIRAZAK). It is recorded and published here as a restricted-access preprint to secure an immutable digital timestamp, establish prior art, and protect the candidate's unique intellectual property rights ahead of formal thesis defense and journal submission.</p> |
| title | THE TECHNICAL ARCHITECTURE OF THE CRAM FRAMEWORK: MODERATING EDUCATOR READINESS FOR AI INTEGRATION IN MALAYSIAN TVET |
| url | https://doi.org/10.5281/zenodo.20309267 |