Investigating the Role of Mindful, Meaningful, and Joyful Learning in Promoting Deep Learning in AI-Based Language Learning
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| Title: | Investigating the Role of Mindful, Meaningful, and Joyful Learning in Promoting Deep Learning in AI-Based Language Learning |
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| Language: | English |
| Authors: | Made Hery Santosa (ORCID |
| Source: | Australian Journal of Applied Linguistics. 2026 9. |
| Availability: | Castledown Publishers. Ground Level, 470 St Kilda Road, Melbourne, 3004, Australia. Tel: +61-3-7003-8355; e-mail: contact@castledown.com; Web site: https://castledown.online/journals/ajal/ |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Second Language Learning, English (Second Language), Student Attitudes, Learner Engagement, Psychological Patterns, Emotional Response, Preservice Teachers, Preservice Teacher Education, Language Teachers, Technology Uses in Education, Gamification, Student Motivation |
| ISSN: | 2209-0959 |
| Abstract: | This study aimed to investigate whether Mindful Learning (MiL), Meaningful Learning (MeL), and Joyful Learning (JL) are predictors of Deep Learning (DL) in AI-based language learning. The study employed a convergent mixed-methods design by combining phenomenology and "ex-post facto" approaches. This study recruited eight EFL students for the interviews and 276 EFL students for the quantitative part. The data were collected through semi-structured interviews, researcher field notes, and questionnaires. The data were analyzed using inductive thematic analysis, descriptive statistics, "Pearson Product Moment," and "Structural Equation Modeling (SEM)." The findings revealed that qualitatively, MiL, MeL, and JL are related to DL. However, the SEM outputs indicated that JL is the only non-predictor. The study offers four implications directed for the use of AI-based applications in facilitating DL in AI-based learning in EFL contexts. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1496282 |
| Database: | ERIC |
| Abstract: | This study aimed to investigate whether Mindful Learning (MiL), Meaningful Learning (MeL), and Joyful Learning (JL) are predictors of Deep Learning (DL) in AI-based language learning. The study employed a convergent mixed-methods design by combining phenomenology and "ex-post facto" approaches. This study recruited eight EFL students for the interviews and 276 EFL students for the quantitative part. The data were collected through semi-structured interviews, researcher field notes, and questionnaires. The data were analyzed using inductive thematic analysis, descriptive statistics, "Pearson Product Moment," and "Structural Equation Modeling (SEM)." The findings revealed that qualitatively, MiL, MeL, and JL are related to DL. However, the SEM outputs indicated that JL is the only non-predictor. The study offers four implications directed for the use of AI-based applications in facilitating DL in AI-based learning in EFL contexts. |
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| ISSN: | 2209-0959 |
