From Technical Competence to Communication Competence: The Mediating Role of Social Competencies in AI-Supported Academic Research
DOI:
https://doi.org/10.46328/ijte.8083Keywords:
student online learning readiness, artificial intelligence tools, technical competence, social competencies, communication competenceAbstract
The increasing integration of artificial intelligence (AI) tools in higher education has transformed students’ approaches to conducting academic research, emphasizing the importance of competencies that support effective AI-assisted research practices. Grounded in the Student Online Learning Readiness (SOLR) framework, this study examines the mediating role of social competencies in the relationship between technical competence and communication competence in students’ use of AI tools for academic research tasks. A quantitative cross-sectional survey was conducted among 342 university students, and the data were analysed using structural equation modelling (SEM). The findings indicate that technical competence significantly predicts both social competence with instructors and social competence with classmates. In turn, these social competencies significantly influence communication competence. However, the direct relationship between technical competence and communication competence was not statistically significant. Bootstrapping analysis further confirmed that social competencies fully mediate the relationship between technical competence and communication competence. These results suggest that technical skills alone are insufficient for developing effective communication abilities when students use AI tools for academic research. Rather, communication competence emerges through meaningful interaction and collaboration with instructors and peers. The study contributes to the literature on AI-supported learning by highlighting the critical role of social competencies in transforming students’ technical readiness into effective communication competence within AI-enhanced academic research environments.
References
Abdalla, R. A. (2024). Examining awareness, social influence, and perceived enjoyment in the TAM framework as determinants of ChatGPT. Personalization as a moderator. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100327.
Adamakis, M., & Rachiotis, T. (2025). Artificial intelligence in higher education: A state-of-the-art overview of pedagogical integrity, artificial intelligence literacy, and policy integration. Encyclopedia, 5(4), 180.
Alshammari, S. H., Alrashidi, M. E., Alshammari, M. H., Alshammari, A. E. A., & Alkhwaldi, A. F. (2025). Determinants of student adoption of artificial intelligence applications in higher education. Scientific Reports, 15(1), 35921.
Arroyo-Machado, W., Ma, J., Chen, T., Johnson, T. P., Islam, S., Michalegko, L., & Welch, E. (2025). Generative AI and academic scientists in US universities: Perception, experience, and adoption intentions. PloS one, 20(8), e0330416.
Awang, P. (2015). SEM made simple: A gentle approach to learning Structural Equation Modeling. MPWS Rich Publication, Bangi.
Baha, T., El Hajji, M., Es-Saady, Y., & Fadili, H. (2024). The impact of educational chatbot on student learning experience. Education and Information Technologies, 29(8), 10153-10176.
Beckman, K., Apps, T., Howard, S. K., Rogerson, C., Rogerson, A., & Tondeur, J. (2025). The GenAI divide among university students: A call for action. The Internet and Higher Education, 101036.
Chien, H. Y., Yeh, Y. C., & Kwok, O. M. (2022). How Online Learning Readiness Can Predict Online Learning Emotional States and Expected Academic Outcomes: Testing a Theoretically Based Mediation Model. Online Learning, 26(4), 193-208.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge.
Dai, K., Liu, Y., & Zhang, X. (2026). Generative AI in higher education: A bibliometric review of emerging trends, power dynamics, and global research landscapes. Computers and Education: Artificial Intelligence, 100544.
Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., & Spirgi, L. (2024). First-year students AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education, 21(1), 18.
Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digital Health, 4(4), e0000764.
Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., & Maur, A. (2024). Prompt engineering as a new 21st century skill. In Frontiers in Education (Vol. 9, p. 1366434). Frontiers.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Garcia, M. B. (2025). ChatGPT as an academic writing tool: factors influencing researchers’ intention to write manuscripts using generative artificial intelligence. International Journal of Human–Computer Interaction, 41(24), 15514-15528.
Gibreel, O., & Arpaci, I. (2025). Development and validation of the prompt engineering competence scale (PECS). Information Development, 02666669251336455.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis.
Hao, Z., Cao, J., Li, R., Yu, J., Liu, Z., & Zhang, Y. (2025). Mapping student-AI interaction dynamics in multi-agent learning environments: Supporting personalised learning and reducing performance gaps. Computers & Education, 105472.
Hughes, L., Malik, T., Dettmer, S., Al-Busaidi, A. S., & Dwivedi, Y. K. (2025). Reimagining higher education: Navigating the challenges of generative AI adoption. Information Systems Frontiers, 1-23.
Jesus, F. S., Ibarra, L. M., Pasion, B. J., Villanueva, W., & Leyesa, M. (2024). ChatGPT as an artificial intelligence learning tool for business administration students in Nueva Ecija, Philippines. International Journal of Learning, Teaching and Educational Research, 23(6), 348-372.
Ke, Q., Gong, Y., & Ke, C. (2025). Bridging AI literacy and UTAUT constructs: Structural equation modeling of AI adoption among Chinese university students. Humanities and Social Sciences Communications, 12(1), 1-13.
Kim, J., Yu, S., Lee, S. S., & Detrick, R. (2025). Students’ prompt patterns and its effects in AI-assisted academic writing: Focusing on students’ level of AI literacy. Journal of Research on Technology in Education, 1-18.
Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225.
Kurtz, G., Amzalag, M., Shaked, N., Zaguri, Y., Kohen-Vacs, D., Gal, E., ... & Barak-Medina, E. (2024). Strategies for integrating generative AI into higher education: Navigating challenges and leveraging opportunities. Education Sciences, 14(5), 503.
Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: a systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(1), 7.
Lee, F. Y., & Chan, T. J. (2024, January). Establishing credibility in AI chatbots: the importance of customization, communication competency and user satisfaction. In 4th International Conference on Communication, Language, Education and Social Sciences (CLESS 2023) (pp. 88-106). Atlantis Press.
Lemke, C., Kirchner, K., Anandarajah, L., & Herfurth, F. N. (2023, October). Exploring the student perspective: Assessing technology readiness and acceptance for adopting large language models in higher education. In 22nd European Conference on e-Learning: ECEL 2023. Academic Conferences and publishing limited.
Liang, Y. D., Chen, S., Abeysekera, R., O'Sullivan, H., Bray, J., & Keevill-Savage, I. (2024). Examining the adoption of technology-enhanced learning in universities and its effects on student performance, satisfaction, and motivation. Computers and Education Open, 7, 100223.
Liu, J. C. (2019). Evaluating Online Learning Orientation Design With a Readiness Scale. Online Learning, 23(4), 42-61.
Luo, R., Husnin, H., & Zaini, M. H. (2025). A systematic review of teachers’ digital competence and its effect on students’ academic self-efficacy, learning engagement and other outcomes. Environment and Social Psychology, 10(9).
Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (2024). Is it the end of the technology acceptance model in the era of generative artificial intelligence?. International Journal of Contemporary Hospitality Management, 36(10), 3324-3339.
Obadă, D. R., Gradinaru, C., & Gradinaru, I. A. (2026). From understanding to influence: the interplay of AI literacy, self-efficacy, and trust in predicting communication students’ AI adoption and word-of-mouth. Frontiers in Communication, 10, 1722464.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891.
Rahman, M. K., Ismail, N. A., Hossain, M. A., & Hossen, M. S. (2025). Students' mindset to adopt AI chatbots for effectiveness of online learning in higher education. Future Business Journal, 11(1), 30.
Ramakrishnan, R., & Thangamuthu, P. (2024). Revolutionizing Campus Communication: NLP-Powered University Chatbots. International Journal of Advanced Computer Science & Applications, 15(6).
Ranade, N., Saravia, M., & Johri, A. (2025). Using rhetorical strategies to design prompts: A human-in-the-loop approach to make AI useful. AI & SOCIETY, 40(2), 711-732.
Razzouki, M., Hammou, S. B., & Izenzal, M. (2025). The adoption and effective use of artificial intelligence in Moroccan higher education: the moderating role of trust. Cogent Education, 12(1), 2553829.
Ren, J., Guo, J., & Li, H. (2025). Linking digital competence, self-efficacy, and digital stress to perceived interactivity in AI-supported learning contexts. Scientific Reports, 15(1), 33182.
Rodriguez-Donaire, S. (2024). Influence of prompts structure on the perception and enhancement of learning through LLMs in online educational contexts. Massive Open Online Courses-Learning Frontiers and Novel Innovations, 2024.
Sallai, D., Cardoso-Silva, J., Barreto, M., Panero, F., Berrada, G., & Luxmoore, S. (2024). Approach generative AI tools proactively or risk bypassing the learning process in higher education. LSE Public Policy Review, 3(3).
Schmidt, D. A., AlBloushi, B., Thomas, A., & Magalhaes, R. (2025). Integrating artificial intelligence in higher education: Perceptions, challenges, and strategies for academic innovation. Computers and education open, 100274.
Shata, A., & Hartley, K. (2025). Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI. International Journal of Educational Technology in Higher Education, 22(1), 14.
Staddon, R. V. (2023). Exploring higher education students’ perspectives on factors affecting use, attitudes and confidence with learning technologies. International Journal of Instruction, 16(2), 31-52.
Sudaryanto, M. R., Hendrawan, M. A., & Andrian, T. (2023). The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students artificial intelligence technology adoption. In E3S Web of Conferences (Vol. 388, p. 04055). EDP Sciences.
Sustaningrum, R., & Haldaka, M. (2025). Student utilization and perceptions of AI technology for academic purposes: a mixed-method analysis. Cogent Education, 12(1), 2553835.
Sanz-Tejeda, A., Domínguez-Oller, J. C., Baldaquí-Escandell, J. M., Gómez-Díaz, R., & García-Rodríguez, A. (2025). The impact of generative AI on academic reading and writing: a synthesis of recent evidence (2023–2025). In Frontiers in Education (Vol. 10, p. 1711718). Frontiers Media SA.
Thanomsing, C., & Sharma, P. (2024). Understanding instructor adoption of social media using the technology acceptance model. Journal of Research in Innovative Teaching & Learning, 17(1), 47-65.
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343.
Wang, H., & Gao, C. (2025). AI utilization, enjoyment, trust and instructor competence as predictors of intercultural communication competence among Chinese students. Scientific Reports.
Wang, Q., Camacho, I., Jing, S., & Goel, A. K. (2022). Understanding the design space of AI-mediated social interaction in online learning: challenges and opportunities. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1-26.
Wen, Q., Wang, J., Guo, Z., & Badulescu, D. (2025). Divergent Role of AI in Social Development: A Comparative Study of Teachers’ and Students’ Perceptions in Online and Physical Classrooms. Behavioral Sciences, 15(12), 1649.
Yakubu, M. N., David, N., & Abubakar, N. H. (2025). Students' behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective. Education and Information Technologies, 30(13), 17969-17994.
Yu, T. (2018). Examining Construct Validity of the Student Online Learning Readiness (SOLR) Instrument Using Confirmatory Factor Analysis. Online learning, 22(4), 277-288.
Yu, T., & Richardson, J. (2015). An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument. Online Learning, 19(5).
Zapata, G. C., Cope, B., Kalantzis, M., Tzirides, A. O., Saini, A. K., Searsmith, D., ... & Abrantes da Silva, R. (2025). AI and peer reviews in higher education: Students’ multimodal views on benefits, differences and limitations. Technology, Pedagogy and Education, 34(5), 583-601.
Zhao, Z., An, Q., & Liu, J. (2025). Exploring AI tool adoption in higher education: Evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support. Frontiers in Psychology, 16, 1619391.
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