Skip to content

The Transformation of Educational Media in the Age of the Algorithm Generation: Adapting Learning to an AI-Based Digital Ecosystem

Sep 2026 · International Journal of Innovative Research in Multidisciplinary Education

Abstract

The rapid advancement of artificial intelligence has fundamentally changed how young people access information and interact with digital technology, giving rise to the term “algorithm generation” a generation whose thinking patterns, preferences, and learning habits are substantially shaped by automated recommendation and content-curation systems. This study addresses three research questions: (1) how educational media is transforming in response to the algorithm generation’s cognitive-behavioral formation; (2) what learning-adaptation strategies are needed for this transformation to move toward augmentation and redefinition rather than inversion of learning; and (3) what challenges accompany this shift and how a human-centered approach can mitigate them. The novelty of this study lies in explicitly positioning “algorithm generation” as an analytic construct rather than an uncritical generational label and in building an integrative conceptual framework linking learners’ cognitive formation, educational media transformation, and multi-level adaptation strategies (teachers, learners, curriculum), an integration rarely found in existing AIED (artificial intelligence in education) reviews, which tend to remain fragmented. The study adopts a qualitative approach with a systematic-integrative literature review design: identification, screening, and eligibility procedures follow PRISMA 2020 logic across 22 primary sources, followed by critical-comparative thematic synthesis. Findings show that educational media is shifting from a static, one-way model toward an adaptive, personalized, data-driven model, yet the direction of this shift toward augmentation/redefinition or toward inversion of learning depends heavily on how seriously adaptation strategies and human-centered, critical, and ethical AI design are actually implemented. This study recommends that the development of educational media in Indonesia explicitly integrate this conceptual framework, particularly during the transition from Kurikulum Merdeka toward deep-learning pedagogy.

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4
#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.

Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8

Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study

An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.

Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al. · 56 citations · ⚡6

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.