Empirical Validation of the ASER Framework for Long-Term Knowledge Retention in Augmented Reality

Long-term knowledge retention remains a critical challenge in augmented reality (AR) learning environments, which often prioritize novelty and short-term engagement over durable learning outcomes. This study empirically validates the Augmented Sensory Experience and Retention (ASER) Framework, an instructional model integrating emotional memory cues, interactive storytelling, and gamification within AR to promote sustained learning. A between-subjects experimental design was conducted with 30 adult participants randomly assigned to either an ASER-based AR condition or a traditional non-AR instructional condition. Baseline equivalence was established using equivalence testing. Learning outcomes were assessed using immediate post-test and three-week delayed recall measures. Individual gain scores were analyzed using Mann–Whitney U tests, and a one-way MANOVA examined multivariate effects across emotional engagement, motivation, learning engagement, and cognitive load. Results revealed significantly greater long-term retention gains in the ASER condition, with a large effect size, alongside stronger short-term improvement. Multivariate analysis demonstrated a significant overall effect of instructional condition, with the ASER group reporting higher engagement, motivation, and emotional involvement, as well as more favorable cognitive load. These findings provide empirical support for the ASER Framework and demonstrate that emotionally enriched, narrative-driven, and gamified AR instruction can foster deeper cognitive processing and more durable knowledge retention than conventional instructional approaches. The study offers evidence-based design guidance for developing pedagogically grounded AR learning systems aimed at sustained educational impact.

@article{Alhebaishi2026,
  title     = {Empirical Validation of the ASER Framework for Long-Term Knowledge Retention in Augmented Reality},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Samer Alhebaishi and Richard Stone and Ulrike Genschel and Kris De Brabanter and Mani Mina and Anthony M. Townsend and Mohammed Ameen},
  doi       = {10.14569/IJACSA.2026.0170301},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170301}
}