IDEAS home Printed from https://ideas.repec.org/a/ibn/hesjnl/v15y2025i4p333.html

AI Assistant Framework on Competency-Based Learning for Digital Competency Development

Author

Listed:
  • Manop Nammanee
  • Thada Jantakoon
  • Rukthin Laoha

Abstract

The accelerating adoption of AI in education highlights the need for an assistant that is explicitly grounded in competency-based learning to develop learners’ digital competencies. This study proposes the AI Assistant Framework on Competency-Based Learning for Digital Competency Development (AICoLED) and evaluates its appropriateness through expert judgment. We synthesized contemporary literature to derive a framework that integrates four inputs (AI technology infrastructure, competency framework, educational content, user interface design), five processes (competency assessment, personalized learning, interactive assistance, competency development, feedback/evaluation), and four outputs (digital competency enhancement, learning achievement, behavioural change, system performance). A structured instrument comprising 44 items across eight domains was rated by eight experts (n = 8) on a 5-point scale. We summarized item- and domain-level means and SDs and mapped means to appropriateness levels. The overall mean across items was 4.69 (SD = 0.49), corresponding to the rating of “Most appropriate.” The section means ranged from 4.63 to 4.75. The highest-rated domain was Innovation and Creativity (Mean = 4.75, SD = 0.44); the lowest was Output Components (Mean = 4.63, SD = 0.62). Top-rated items included content competency alignment (1.1.3), systematic linkage of inputs (1.1.5), accuracy and coverage of competency assessment (1.2.1), framework novelty (4.1), and currency of NLP use (6.1.1) (all Means = 4.88, SD = 0.35). Items with greater dispersion concerned system indicators and competency standards (1.3.2-1.3.4; 6.3.2-6.3.3), with SD up to 0.76. Expert appraisal indicates that AICoLED is conceptually straightforward, pedagogically coherent, and technically feasible; however, the measurement components (output indicators and competency standards) require tighter operationalization before pilot deployment. Future work should pilot the framework in authentic contexts, validate measurement models, and assess effectiveness and scalability.

Suggested Citation

  • Manop Nammanee & Thada Jantakoon & Rukthin Laoha, 2025. "AI Assistant Framework on Competency-Based Learning for Digital Competency Development," Higher Education Studies, Canadian Center of Science and Education, vol. 15(4), pages 333-333, November.
    • Handle: RePEc:ibn:hesjnl:v:15:y:2025:i:4:p:333
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/hes/article/download/0/0/52313/56967
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/hes/article/view/0/52313
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tarattakan Pachumwon & Narudon Rudto & Kunawut Boonkwang & Bhibul Hongthong & Thada Jantakoon, 2025. "A Bibliometric Analysis of Digital Literacy in Remote Learning," Higher Education Studies, Canadian Center of Science and Education, vol. 15(2), pages 1-54, May.
    2. Thada Jatnkoon & Kitsadaporn Jantakun & Thiti Jantakun & Rungfa Pasmala, 2025. "STEAM Micro-learning Model based on Massive Open Online Courses with Augmented Reality Technology to enhance Creativity and Innovation," Higher Education Studies, Canadian Center of Science and Education, vol. 15(2), pages 321-321, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Potsirin Limpinan & Thada Jantakoon & Somsuk Trisupakitti, 2025. "Technology Acceptance Model of Immersive Microlearning in STEAM Education: Insights from a PLS-SEM Analysis," Higher Education Studies, Canadian Center of Science and Education, vol. 15(4), pages 1-85, November.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:hesjnl:v:15:y:2025:i:4:p:333. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.