Integrative Analysis of ADDIE, ARCS, and ASSURE: Toward a Hybrid Framework for Technology-Enhanced Instructional Design
Bibliographic record
Abstract
Through the perspective of technology integration, this research addresses the limited cross-model analysis that exists in the field of educational technology scholarship. The paper does so by methodically reviewing and comparing three notable instructional design models: ADDIE, ARCS, and ASSURE. In this review, a structured thematic synthesis is utilized to evaluate the affordances, limitations, and adaptability of each model for technology-enhanced learning in K–12, higher education, and professional development contexts. The review draws on studies that have been peer-reviewed and published between the years 2010 and 2024. These studies were retrieved from Web of Science, ERIC, and Google Scholar. According to the findings of the analysis, ADDIE provides comprehensive structural planning and iterative evaluation, but it requires a significant amount of time and resources. ARCS excels in maintaining learner motivation, but it does not have explicit sequencing for technology-rich curricula. ASSURE offers clear, technology-oriented procedural guidance, but it faces challenges in terms of scalability in settings with limited resources. For the purpose of strengthening both the theoretical and practical ability for equitable and scalable technology adoption, the research presents a hybrid framework that blends ADDIE’s systematic design, ARCS’s motivating methods, and ASSURE’s media integration processes. It offers implications that can be put into practice for the development of curricula, the preparation of teachers, and policy interventions that are aimed at fostering education that is both inclusive and enabled by technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".