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  • ChatGPT as a coping mechanism: Postgraduate use under supervisory gaps in South-South Nigerian tertiary institutions

    Uka Nwagbara1, Daramfon Okon2, Precious Ukeje3
    1. University of Uyo , NG
    2. University of Uyo , NG
    3. University of Uyo , NG
    2026-07-02
    doi https://doi.org/10.56003/jaieli.v2i1.711
    Published Online: 02 July 2026
    |
    page 1-13
    Abstract

    Background: Generative AI tools like ChatGPT are common in higher education, especially where students lack timely academic support. In many South-South Nigerian universities, gaps in postgraduate supervision may influence how students use such tools.

    Objectives: This study examines how postgraduate students become aware of ChatGPT, how much they use it, and why they rely on it when supervision is limited.

    Methods: A qualitative exploratory design was used. Twenty-one postgraduate students were purposively selected from universities in South-South Nigeria. Data were collected through online semi-structured interviews using a validated guide. Inductive thematic analysis, using Braun & Clarke’s framework, was applied to identify patterns in the data.

    Results: Findings show that awareness of ChatGPT is mostly informal, driven by peers, social media, and personal trial and error. Many students use ChatGPT frequently, especially during proposal writing, data analysis, and thesis development. Its use is strongly linked to delays or gaps in supervision.

    Conclusions: ChatGPT is widely used as a support tool and coping strategy, rather than merely a convenience. Its role reflects gaps in supervision and the need for clearer institutional guidance and training.

    APA Citation Style
    Nwagbara, U. , Okon, D. , & Ukeje, P. (2026). ChatGPT as a coping mechanism: Postgraduate use under supervisory gaps in South-South Nigerian tertiary institutions. Journal of Artificial Intelligence in Education & Learning Innovation, 1-13. https://doi.org/10.56003/jaieli.v2i1.711

  • Academic goals and learning strategies of pre-service teachers during emergency online learning in the Philippines

    Swen Joshryll Acebes1, Jaizel Malitante2, Nikki Tuble3, Rowena Bongolto4
    1. Mindanao State University - General Santos , PH
    2. Mindanao State University - General Santos , PH
    3. Mindanao State University - General Santos , PH
    4. Mindanao State University - General Santos , PH
    2025-10-08
    doi https://doi.org/10.56003/jaieli.v1i1.569
    Published Online: 08 October 2025
    |
    page 32-49
    Abstract

    Background: The COVID-19 pandemic has lasted an extended period, during which many students have reported being adversely affected by the escalating global crisis.

    Objectives: This study examined the academic goals, learning barriers, facilitative strategies, and ways forward of pre-service teachers during emergency online learning.

    Methods: This qualitative case study employed content analysis to examine data gathered through focus group discussions. To explore the implications of survey findings and gain deeper insights into students' perspectives on remote learning, the researchers conducted Focus Group Discussions (FGDs) with twelve purposively selected pre-service teachers via Google Meet. The discussion transcripts were analyzed to construct a compelling narrative, supported by literature and prior research, to explain emerging themes.

    Results: Among the twelve participants, ten reported experiencing psychosocial stress, eight cited recurring technical issues, and seven mentioned financial constraints as significant learning barriers. Despite these challenges, the participants remained motivated to complete their coursework, viewing academic achievement as a stepping stone toward improving their socio-economic conditions. They employed various strategies, including scheduling, note-taking, goal-setting, optimistic thinking, parental scaffolding, and systematic planning, to adapt to online learning. To ensure credibility, the researchers used member checking, peer debriefing, and FGD and survey data triangulation.

    Conclusions: These findings highlight the resilience and adaptability of pre-service teachers in navigating the challenges of emergency online learning, underscoring the need for supportive institutional policies and resources. Future research may consider comparing student experiences in public and private institutions or examining the long-term impact of emergency remote learning on teaching efficacy. Pedagogical and academic implications are further discussed.

    APA Citation Style
    Acebes, S. J. , Malitante, J. , Tuble, N. , & Bongolto, R. (2025). Academic goals and learning strategies of pre-service teachers during emergency online learning in the Philippines. Journal of Artificial Intelligence in Education & Learning Innovation, 32-49. https://doi.org/10.56003/jaieli.v1i1.569

  • The future of teaching: Analyzing the interplay between AI literacy and TPACK among BEED pre-service teachers

    Karl Alvin Aglibot1, Chelsea Lorraine Rito2, Larence Villalon3, Nelvin Gampal 4
    1. Mindanao State University - General Santos , PH
    2. Mindanao State University – General Santos , PH
    3. Mindanao State University – General Santos , PH
    4. Mindanao State University – General Santos , PH
    2025-10-08
    doi https://doi.org/10.56003/jaieli.v1i1.570
    Published Online: 08 October 2025
    |
    page 50-65
    Abstract

    Background: As artificial intelligence (AI) continues transforming the educational landscape, pre-service teachers must develop theoretical understanding and practical skills in AI integration.

    Objectives: This study explored the relationship between AI literacy and TPACK among 139 BEED pre-service teachers.

    Methods: Using descriptive statistics, Spearman's correlation, and multiple linear regression analysis, the study investigated how components of AI literacy relate to TPACK.

    Results: Results revealed that the pre-service teachers demonstrated high levels of AI literacy, particularly in ethical awareness and intrinsic motivation. Pre-service teachers expressed strong interest in AI's educational potential and a commitment to its responsible use. While their foundational AI knowledge was high, many reported moderate self-efficacy and lacked confidence in executing AI-related tasks. Gaps were also noted in applying AI concepts to real-world teaching scenarios and designing AI-driven solutions. Furthermore, the results showed high TPACK proficiency, with strength in pedagogical and technological knowledge, though weaknesses were observed in content areas like mathematics and technical troubleshooting. A moderate positive correlation (Spearman's ρ = 0.48, p < 0.05) between AI literacy and TPACK indicates a meaningful association. Regression analysis revealed that AI literacy components explained 25.5% of the variance in TPACK. Cognitive learning emerged as the only significant predictor.

    Conclusions: The findings underscore the need for teacher education programs to provide hands-on, cognitive-focused AI training. Hence, enhancing pre-service teachers' cognitive and practical skills is essential to thoroughly preparing them for AI-enhanced teaching environments' demands.

    APA Citation Style
    Aglibot, K. A. , Rito, C. L. , Villalon, L. , & Gampal , N. (2025). The future of teaching: Analyzing the interplay between AI literacy and TPACK among BEED pre-service teachers. Journal of Artificial Intelligence in Education & Learning Innovation, 50-65. https://doi.org/10.56003/jaieli.v1i1.570

  • Assessing academic integrity patterns among pre-service teachers using AI-based plagiarism detection

    Irish Arroyo1, Sittie Nor Aisha Rusiana2, Daniere Maryje Tabada3, Kim Silva4, Carla Marie Rubio5, Esmeraldo Sarad6, Cathy Mae Toquero7
    1. College of Education, Mindanao State University-General Santos , PH
    2. College of Education, Mindanao State University-General Santos , PH
    3. College of Education, Mindanao State University-General Santos , PH
    4. College of Education, Mindanao State University-General Santos , PH
    5. College of Education, Mindanao State University-General Santos , PH
    6. College of Education, Mindanao State University-General Santos , PH
    7. Faculty of Psychology, Malang State University , ID
    2026-04-06
    doi https://doi.org/10.56003/jaieli.v1i2.685
    Published Online: 06 April 2026
    |
    page 148-163
    Abstract

    Background: The advent of artificial intelligence has intensified concerns about academic dishonesty among students, particularly in written outputs. Plagiarism, a common form of misconduct, involves using others’ ideas without proper attribution.

    Objectives: This study aimed to determine the degree and patterns of academic integrity in the pre-service teachers’ book reviews.

    Methods: Employing a descriptive research design through document analysis, the study used purposive sampling to collect 40 book reviews, applying set inclusion and exclusion criteria. An AI-based plagiarism detection tool, Grammarly, was used to identify instances of plagiarism and assess the level of academic integrity reflected in the outputs. Descriptive statistical methods were applied to examine plagiarism levels across different sections of the book reviews.

    Results: Results showed that the majority of pre-service teachers demonstrated a very high level of academic integrity in their book reviews, scoring 97% or interpreted as students committing 3% plagiarism. Furthermore, sectional analysis showed that the introduction and conclusion exhibited higher integrity, while the body contained the most instances of plagiarism. This suggests that students struggled more with sections requiring critical thinking, original insights, and proper citation. Most plagiarism cases were linked to failure to cite sources and unintentional misuse of references.

    Conclusions: Teacher Education Institutions integrate AI-supported evaluation tools and plagiarism detection systems into instruction and assessment. Embedding academic integrity modules and discussions on AI ethics is also encouraged. Future research should involve larger and more diverse samples and utilize multiple AI detection tools to enhance the reliability and validity of findings through cross-verification.

    APA Citation Style
    Arroyo, I. , Rusiana, S. N. A. , Tabada, D. M. , Silva, K. , Rubio, C. M. , Sarad, E. , & Toquero, C. M. (2026). Assessing academic integrity patterns among pre-service teachers using AI-based plagiarism detection. Journal of Artificial Intelligence in Education & Learning Innovation, 148-163. https://doi.org/10.56003/jaieli.v1i2.685
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