Development of an Artificial Intelligence Integrated Learning Ecosystem for Advancing Computational Thinking and Digital Literacy Skills

Authors

  • Fathul Mufid Universitas Islam Negeri Sunan Kudus
  • Muhammad Munadi Universitas Islam Negeri Raden Mas Said Surakarta
  • Aliah Bagus Purwakania Hasan Universitas Al Azhar Indonesia
  • Teti Berliani Universitas Palangkaraya

DOI:

https://doi.org/10.55927/jeda.v5i2.26

Keywords:

Artificial Intelligence, Computational Thinking, Digital Literacy, Learning Ecosystem, Higher Education.

Abstract

The integration of Artificial Intelligence (AI) in higher education has created opportunities to strengthen computational thinking and digital literacy skills through adaptive learning environments. This study aims to develop an AI-integrated learning ecosystem and examine its effectiveness in improving students’ computational thinking and digital literacy competencies. The research employed a mixed methods approach using a Design-Based Research framework involving 80 university students in Jawa Tengah selected through purposive sampling. Data were collected through computational thinking tests, digital literacy questionnaires, observations, and interviews during the implementation process. Quantitative data were analyzed using paired sample t-tests, while qualitative data were examined through thematic analysis. The findings revealed that the developed ecosystem significantly improved students’ problem-solving abilities, digital literacy, and learning engagement through adaptive AI-based learning support. This study contributes to the advancement of AI-based educational innovation in higher education and provides practical implications for developing future-oriented digital learning systems.

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Published

2026-06-07

How to Cite

Fathul Mufid, Muhammad Munadi, Aliah Bagus Purwakania Hasan, & Teti Berliani. (2026). Development of an Artificial Intelligence Integrated Learning Ecosystem for Advancing Computational Thinking and Digital Literacy Skills. Journal of Educational Analytics, 5(2), 564–583. https://doi.org/10.55927/jeda.v5i2.26

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Articles