Artificial Intelligence-Supported Workplace Education: A Systematic Review of Learning Outcomes, Opportunities, and Challenges

Background/purpose. In the digital age, education is no longer considered an activity carried out during a specific period of life, but rather a lifelong concept. The skills acquired through formal education processes can become obsolete within a few years due to the rapid pace of the era. It is precisely at this point that corporate structures are under more pressure than ever to keep their employees' skills up to date. These training programmes are conducted with intensive use of educational technologies in order to prevent workforce loss and provide training at an appropriate cost. In the field of corporate learning and development, the impact of artificial intelligence technologies on employee training is also increasing.

Materials/methods. This article systematically examines the effects, opportunities, and challenges of AI applications in employee training. The field of artificial intelligence applications in education has been evaluated within a theoretical framework, and articles published between 2020 and 2025 have been analysed using a systematic review and a thematic content analysis, focusing on impacts, opportunities, and challenges.

Results. The analysis revealed that artificial intelligence affects cognitive, affective, behavioural, and technical-organisational dimensions of employee training. Opportunities were reported in learning quality, cognitive development, affective and social aspects, performance and efficiency, and organisational aspects, while challenges were reported in technical, infrastructure, ethics and law, organisational, and pedagogical dimensions.

Conclusion. Artificial intelligence is expected to make significant contributions to employee training. However, many areas identified as challenges need to be addressed in order to maximise the benefits of these technologies.

Keywords: Employee training, artificial intelligence, corporate learning, adaptive learning, learning analytics

References

Abel, U., Emmanuel, C., & Pascal, U. O. (2024). Leveraging AI-Powered chatbots to enhance customer service efficiency and future opportunities in automated support. Computer Science, 5(10), 2485-2510. https://doi.org/10.51594/csitrj.v5i10.1676

Al Husseiny, F. (2023). Artificial intelligence in higher education: A new horizon. In S. Kaddoura (Ed.), Handbook of research on ai methods and applications in computer engineering (pp. 295-315). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-6937-8.ch014

Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215. https://doi.org/10.1016/j.caeai.2024.100215

Alshahrani, A. (2025). SMOTE-Optimized Machine Learning Framework for Predicting Retention in Workforce Development Training. Computers, Materials & Continua, 85(2). https://doi.org/10.32604/cmc.2025.065211

Ans, M., Montanaro, T., Sergi, I., Troisi, G., Sponziello, A., Pezzuto, M., & Patrono, L. (2025). Design of an innovative solution to integrate and orchestrate IoT technologies with chatbots for smart home automation. Internet of Things, 33, 101693. https://doi.org/10.1016/j.iot.2025.101693

Argote, L. (2012). Organizational learning: Creating, retaining and transferring knowledge. New York, NY: Springer.

Argyris, C., & Schön, D. A. (1997). Organizational learning: A theory of action perspective. Reis: Revista Espanola de Investigaciones Sociologicas, (77/78), 345-348. https://doi.org/10.2307/40183951

Assayed, S. K., Alkhatib, M., & Shaalan, K. (2023). Advising chatbot for high school in smart cities. In 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-6). IEEE. https://doi.org/10.23919/SpliTech58164.2023.10193065

Assayed, S. K., Alkhatib, M., Shaalan, K. F., & Alsayed, S. A. (2025). HSGAdviser: AI speech assistant for enabling sustainable education solutions. In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCIAA65327.2025.11013058

Babaeian-Jelodar, M. (2025). Generative AI, Large Language Models, and ChatGPT in Construction Education, Training, and Practice. Buildings, 15(6), 933. https://doi.org/10.3390/buildings15060933

Barthakur, A., Joksimovic, S., Kovanovic, V., Mello, R. F., Taylor, M., Richey, M., & Pardo, A. (2022). Understanding depth of reflective writing in workplace learning assessments using machine learning classification. IEEE Transactions on Learning Technologies, 15(5), 567-578. https://doi.org/10.1109/TLT.2022.3162546

Barthakur, A., Kovanovic, V., Joksimovic, S., Zhang, Z., Richey, M., & Pardo, A. (2022). Measuring leadership development in workplace learning using automated assessments: Learning analytics and measurement theory approach. British Journal of Educational Technology, 53(6), 1842-1863. https://doi.org/10.1111/bjet.13218

Bayly-Castaneda, K., Ramirez-Montoya, M. S., & Morita-Alexander, A. (2024). Crafting personalized learning paths with AI for lifelong learning: a systematic literature review. Frontiers in Education, 9:1424386. https://doi.org/10.3389/feduc.2024.1424386

Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition. The New Media Consortium. https://www.learntechlib.org/p/174879

Bessen, J. E. (2018). AI and jobs: The role of demand. National Bureau of Economic Research, Working Paper 24235. https://doi.org/10.3386/w24235

Bharwaney, R., Martinez, S. A., Simko, B., Dalto, J., Bangs, T., & Lovett, N. (2025). Empowering Organizational Learning: Technology-Driven Performance Support for Real-Time Skill Building and Workflow Efficiency. TechTrends, 1-17. https://doi.org/10.1007/s11528-025-01101-0

Bhoir, S. V., Patil, S. R., & Mogul, I. Y. (2022). Person-based automation with artificial intelligence Chatbots: A driving force of Industry 4.0. In Artificial Intelligence and Industry 4.0 (pp. 215-244). Academic Press. https://doi.org/10.1016/B978-0-323-88468-6.00003-6

Binet, A., Simon, T., & Kite, E. S. (Trans). (1916). New methods for the diagnosis of the intellectual level of subnormals. (L'Année Psychologique, 1905, Vol. XII, 191-244). In The development of intelligence in children (The Binet-Simon Scale) (pp. 37-90). Baltimore, MD, US: Williams & Wilkins Co. https://doi.org/10.1037/11069-002

Buame, J. A., Tiika, B. J., & Lotsu, S. A. (2025). The role of AI in shaping the discharge of duties of university administrators: A systematic review. Pan-African Journal of Education and Social Sciences, 6(2), 13–31. https://doi.org/10.56893/pajes2025v06i02.02

Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3-26. https://www.jstor.org/stable/10.2307/jeductechsoci.15.3.3

Buckingham Shum, S., & Luckin, R. (2019). Learning analytics and AI: Politics, pedagogy and practices. British Journal of Educational Technology, 50, 2785–2793. https://doi.org/10.1111/bjet.12880

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510

Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383

Dang, N. T. T., Ha, V. D., & Ho, T. T. N. (2024). Challenges and solutions for human resource development activities of the FPT Group in the age of digital technology. Multidisciplinary Science Journal, 7(4), 2025152. https://doi.org/10.31893/multiscience.2025152

Daniel, B. K. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230

Depoo, L., Hajerová-Mullerová, L., Kronberger, Z., Říhová, G., Stříteský, M., Hořáková, M., Legnerová, K., Palíšková, M., Němec, O., Šmíd, D., Jurčík, T., & Kopecký, M. (2025). The Impact of AI Implementation on Job Transformation and Competency Requirements: Prioritising Reskilling and Soft Skills Development. Quality Innovation Prosperity, 29(2), 71–89. https://doi.org/10.12776/qip.v29i2.2165

Dhilipan, C., Kannan, A. S., & Elamurugan, B. (2025). Addressing AI anxiety: workforce development strategies for an AI-driven era. International Journal of System Assurance Engineering and Management, 16, 4131–4141. https://doi.org/10.1007/s13198-025-02916-z

DiDonna, N., Shetty, P. N., Khan, K., & Damitz, L. (2024). Unveiling the potential of AI in plastic surgery education: a comparative study of leading AI platforms’ performance on in-training examinations. Plastic and Reconstructive Surgery–Global Open, 12(6), e5929. https://doi.org/10.1097/GOX.0000000000005929

Ekuma, K. (2023). Artificial Intelligence and Automation in Human Resource Development: A Systematic Review. Human Resource Development Review, 23(2), 199-229. https://doi.org/10.1177/15344843231224009

El Shazly, R. (2021). Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study. Expert Systems, 38(3):e12667. https://doi.org/10.1111/exsy.12667

Enakrire, R. T., Fombad, M. C., & Morodi, L. (2025). Skills required of academics to use digital technologies in open distance learning institutions. Innovative Higher Education, 50(3), 843-866. https://doi.org/10.1007/s10755-024-09758-w

Eurostat. (2025, 23 Ocak). Usage of AI technologies increasing in EU enterprises. Online available: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20250123-3

Gallagher, T., Slof, B., van der Schaaf, M., Arztmann, M., Fracaro, S. G., & Kester, L. (2024). Learning analytics dashboard design: Workplace learner preferences for reference frames in immersive training in practice. Journal of Computer Assisted Learning, 40(6), 2840–2855. https://doi.org/10.1111/jcal.13042

Gezdur, A., & Bhattacharjya, J. (2025). Innovators and transformers: Enhancing supply chain employee training with an innovative application of a large language model. International Journal of Physical Distribution & Logistics Management, 55(4), 394–408. https://doi.org/10.1108/IJPDLM-12-2023-0492

Gladwin-Geoghegan, R., & Thompson, C. (2021). Legacy of lockdown: Exploring the opportunities for development in legal education as a consequence of the COVID-19 pandemic. Journal of Ethics and Legal Technologies, 3(1), 5–26. https://doi.org/10.14658/pupj-JELT-2021-1-2

Goel, A., Dede, C., Garn, M., & Ou, C. (2024). AI‐ALOE: AI for reskilling, upskilling, and workforce development. AI Magazine, 45(1), 77-82. https://doi.org/10.1002/aaai.12157

Goldstein, I. L., & Ford, J. K. (2002). Training in organizations: Needs assessment, development, and evaluation (4th ed.). Wadsworth/Thomson Learning.

Gupta, R., Herzog, I., Park, J. B., Weisberger, J., Firouzbakht, P., Ocon, V., Chao, J., Lee, E. S., & Mailey, B. A. (2023). Performance of ChatGPT on the plastic surgery inservice training examination. Aesthetic surgery journal, 43(12), NP1078-NP1082. https://doi.org/10.1093/asj/sjad128

Hassan, A. (2022). The influence of artificial intelligence on smart learning: An overview. In A. Hamdan, A. E. Hassanien, T. Mescon & B Alareeni (Eds.), Technologies, artificial intelligence and the future of learning post-COVID-19: The crucial role of international accreditation (pp. 591–615). https://doi.org/10.1007/978-3-030-93921-2_31

Hitt, M. A., Bierman, L., Shimizu, K., & Kochhar, R. (2001). Direct and moderating effects of human capital on strategy and performance in professional service firms: A resource-based perspective. Academy of Management journal, 44(1), 13-28. https://doi.org/10.2307/3069334

Hoelscher, S. H., & Pugh, A. (2025). NURSES embracing artificial intelligence: A guide to artificial intelligence literacy for the nursing profession. Nursing Outlook, 73(4), 102466. https://doi.org/10.1016/j.outlook.2025.102466

Hong, L. (2025). Development and validation of a competency-based ladder pathway for AI literacy enhancement among higher vocational students. Scientific Reports, 15(1), 29898. https://doi.org/10.1038/s41598-025-15202-6

Hsu, T. C., Huang, H. L., Hwang, G. J., & Chen, M. S. (2023). Effects of incorporating an expert decision-making mechanism into chatbots on students’ achievement, enjoyment, and anxiety. Educational Technology & Society, 26(1), 218–231. https://doi.org/10.30191/ETS.202301_26(1).0016

Humar, P., Asaad, M., Bengur, F. B., & Nguyen, V. (2023). ChatGPT is equivalent to first-year plastic surgery residents: evaluation of ChatGPT on the plastic surgery in-service examination. Aesthetic Surgery Journal, 43(12), NP1085-NP1089. https://doi.org/10.1093/asj/sjad316

Hwang, G. J., & Chang, C. Y. (2021). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31(7), 4099–4112. https://doi.org/10.1080/10494820.2021.1952615

Hwang, G. J., Spikol, D., & Li, K.-C. (2018). Guest editorial: Trends and research issues of learning analytics and educational big data. Educational Technology & Society, 21(2), 134–136. https://www.jstor.org/stable/10.2307/26388386

Jain, N., Gottlich, C., Fisher, J., Campano, D., & Winston, T. (2024). Assessing ChatGPT’s orthopedic in-service training exam performance and applicability in the field. Journal of Orthopaedic Surgery and Research, 19(1), 27. https://doi.org/10.1186/s13018-023-04467-0

Jia, X., & Hou, Y. (2024). Architecting the future: exploring the synergy of AIdriven sustainable HRM, conscientiousness, and employee engagement. Discover Sustainability, 5. https://doi.org/10.1007/s43621-024-00214-5

Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research landscape of adaptive learning in education: A bibliometric study on research publications from 2000 to 2022. Sustainability, 15(4), 3115. https://doi.org/10.3390/su15043115

Johnson, C. C., Harold, S., Chestnut, J., Glover, K., & Walton, J. B. (2025). Addressing the Artificial Intelligence (AI) Talent Gap: Outcomes of the First US Nationally-Registered AI Apprenticeship Program. Education Sciences, 15(3), 324. https://doi.org/10.3390/educsci15030324

Kabudi, T., Pappas, I. O., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic review of empirical evidence. Computers & Education, 168, 104195. https://doi.org/10.1016/j.caeai.2021.100017

Kaszalik, Z., Kövecses, V. G., & Szombathelyi, M. K. (2025). Overview on the Sustainable and Responsible Educational Technology Efforts Using Artificial Intelligence for the Workers of the Future. Journal of Sustainability Research, 7(2):e250039. https://doi.org/10.20900/jsr20250039

Khosravi, H., Viberg, O., Kovanovic, V., & Ferguson, R. (2023). Generative AI and learning analytics. Journal of Learning Analytics, 10(3), 1-6. https://doi.org/10.18608/jla.2023.8333

Khot, A., & Goyal, D. R. (2025). A Study of Enhancing Training Effectiveness ‎Personalized Employee-Development through ‎Generative AI (West Region Mid-Management IT ‎Employees-India)‎. International Journal of Accounting and Economics Studies, 12(5), 710-718. https://doi.org/10.14419/mng2fd65

Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology, 50, 2855-2870. https://doi.org/10.1111/bjet.12868

Knowles, M. S. (1984). Andragogy in action. Applying modern principles of adult education. San Francisco, CA: Jossey Bass.

Li, J., & Yeo, R. K. (2024). Artificial intelligence and human integration: a conceptual exploration of its influence on work processes and workplace learning. Human Resource Development International, 27(3), 367–387. https://doi.org/10.1080/13678868.2024.2348987

Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review (Online). https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education

Love, A. S., Niu, C., & Labay-Marquez, J. (2025). Artificial Intelligence in Public Health Education: Navigating Ethical Challenges and Empowering the Next Generation of Professionals. Health Promotion Practice, 15248399251320989. https://doi.org/10.1177/15248399251320989

Martinez-Marroquin, E., & Male, S. (2021). Microcredentials for recognition of workplace learning”, Journal of Teaching and Learning for Graduate Employability, 12(1), 52-57. https://doi.org/10.21153/jtlge2021vol12no1art1513

Martinez-Marroquin, E., & Senadji, B. (2025). Activity theory as framework for analysis of workplace learning technologies: the case of generative AI conversational agents. International Journal of Information and Learning Technology, 42(4). 353–365. https://doi.org/10.1108/IJILT-07-2024-0141

McKinsey. (2025). The state of AI: Global survey 2025. Online available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Menshikova, M., Bonacci, I., Scarozza, D., Fedorova, A., & Ghazy, K. (2025). Social Interaction with Collaborative Robots in the Hotel Industry: Analysing the Employees’ Perception. Communications of the Association for Information Systems, 57(1), 56. Retrieved from https://aisel.aisnet.org/cais/vol57/iss1/

Merino-Campos, C. (2025). The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review. Trends in Higher Education, 4(2), 17. https://doi.org/10.3390/higheredu4020017

Meyen, M., Pfaff-Rüdiger, S., Dudenhöffer, K., & Huss, J. (2010). The internet in everyday life: A typology of internet users. Media, Culture & Society, 32(5), 873–882. https://doi.org/10.1177/0163443710374792

Mitsugi, N., Ijuin, K., Oshiyama, C., Hara, E., & Nishimura, T. (2025). An AI-mediated framework for recursive learning: transforming individual experiences into organizational knowledge and autonomous engagement in elderly care. Frontiers in Digital Health, 7, 1529072. https://doi.org/10.3389/fdgth.2025.1529072

Mohammed, I. A., Bello, A., & Ayuba, B. (2025). Effect of large language models artificial intelligence ChatGPT chatbot on achievement of computer education students. Education and Information Technologies, 30, 11863–11888. https://doi.org/10.1007/s10639-024-13293-8

Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The Impact of Artificial Intelligence on Workers’ Skills: Upskilling and Reskilling in Organisations. Informing Science, 26, 39–68. https://doi.org/10.28945/5078

Nwabekee, U. S., Okpeke, F., & Onalaja, A. E. (2025). Modeling AI-enhanced customer experience: The role of chatbots and virtual assistants in contemporary marketing. World Scientific News, 203, 54-77.

OECD. (2019). Scoping the OECD AI principles: Deliberations of the expert group on artificial intelligence ant the OCED (AIGO) [OECD digital economy papers, No. 291]. OECD Publishing.

Olanrewaju, O. I. K., Daramola, G. O., & Babayeju, O. A. (2024). Transforming business models with ESG integration: A strategic framework for financial professionals. World Journal of Advanced Research and Reviews, 22(3), 554-563. https://doi.org/10.30574/wjarr.2024.22.3.1757

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Pammer-Schindler, V., & Rosé, C. (2022). Data-Related Ethics Issues in Technologies for Informal Professional Learning. International Journal of Artificial Intelligence in Education, 32, 609–63. https://doi.org/10.1007/s40593-021-00259-x

Phillip-Durham, G., & Deasey Weinstein, T. (2025). Harnessing Artificial Intelligence for educational reform and workforce development: the case of the Cayman Islands. The Round Table, 114(4), 379–398. https://doi.org/10.1080/00358533.2025.2507802

Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10:e39630. https://doi.org/10.1016/j.heliyon.2024.e39630

Rawashdeh, A. (2025). The consequences of artificial intelligence: An investigation into the impact of AI on job displacement in accounting. Journal of Science and Technology Policy Management, 16(3), 506–535. https://doi.org/10.1108/JSTPM-02-2023-0030

Rizzo, M. G., Cai, N., & Constantinescu, D. (2024). The performance of ChatGPT on orthopaedic in-service training exams: A comparative study of the GPT-3.5 turbo and GPT-4 models in orthopaedic education. Journal of Orthopaedics, 50, 70-75. https://doi.org/10.1016/j.jor.2023.11.056

Romanyukha, A., Mazloumi, M., De Waelheyns, T., Mishra, N., Jacobs, J., & Fitousi, N. (2025). Development of a context-aware integrated training module based on large language models for continuous education in radiation protection. Physica Medica, 137, 105090. https://doi.org/10.1016/j.ejmp.2025.105090

Rozman, M., Tominc, P., & Milfelner, B. (2023). Maximizing employee engagement through artificial intelligent organizational culture in the context of leadership and training of employees: Testing linear and non-linear relationships. Cogent Business Management, 10(2):2248732. https://doi.org/10.1080/23311975.2023.2248732

Ruiz-Calleja, A., Prieto, L. P., Ley, T., Rodríguez-Triana, M. J., & Dennerlein, S. (2021). Learning analytics for professional and workplace learning: A literature review. IEEE Transactions on Learning Technologies, 14(3), 353-366. https://doi.org/10.1109/TLT.2021.3092219

Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach, (4th US ed). https://aima.cs.berkeley.edu/index.html

Sabale, A., & Gomathi, S. (2022). Role of Artificial Intelligence in Corporate Training and Development-A Conceptual Paper. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 590-595. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2328

Schwab, K. (2017). The Fourth Industrial Revolution. New York:Currency Books.

Seneca, C. N., McCarn, B. M., & Baker, L. (2025). Responsibility to All Relations: Indigenous Evaluation of an AI/AN Public Health Workforce Development Program. Canadian Journal of Program Evaluation, 39(3), 526-546. https://doi.org/10.3138/cjpe-2025-0008

Shamaylah, N., Mohammad, S. I., Al Oraini, B., Al-Gaafreh, J. M. Y., Alqahtani, M. M., Vasudevan, A., ... & Hunitie, M. F. A. (2025). Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions. Data and Metadata, 4, 886-886. https://doi.org/10.56294/dm2025886

Shannaq, B., Alabri, A., Sriram, V. P., & Ali, O. B. (2025). Investigating the impact of AI on the workforce and the future of work in the region: A machine learning perspective. Bangladesh Journal of Multidisciplinary Scientific Research, 10(4), 1-10. https://doi.org/10.46281/a7zcns96

Shrivastava, N., Tewari, P., Sujatha, S., Bogireddy, S. R., Varshney, N., & Sharma, V. (2025). Natural language processing for conversational AI: Chatbots and virtual assistants. In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 3, pp. 1-6). IEEE. https://doi.org/10.1109/IATMSI64286.2025.10984818

Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi.org/10.1177/0002764213479366

Srirevathi, S. D., & Aravind, B. R. (2025). Bridging the Digital Divide: AI-Based In-Service Training for Teachers of Tribal Students. Asian Journal of University Education, 21(3), 776-778. https://doi.org/10.24191/ajue.v21i3.52

Tempelaar, D., Rienties, B., & Nguyen, Q. (2021). The contribution of dispositional learning analytics to precision education. Educational Technology & Society, 24(1), 109-122. https://www.jstor.org/stable/10.2307/26977861

Togambayev, R., Pirmanov, I., Koshekov, K., Levchenko, N., Koshekov, A., Tanovitskiy, Y., & Kurbanov, Y. (2025). Digitalization of the educational process: synergy of gamification and artificial intelligence in the training of highly qualified civil aviation specialists. Radioelectronic and Computer Systems, 2025(2), 5-21. https://doi.org/10.32620/reks.2025.2.01

Tsai, Y. S., Perrotta, C., & Gaševic, D. (2020). Empowering learners with personalized learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45, 554–567. https://doi.org/10.1080/02602938.2019.1676396

Tsai, Y. S., Poquet, O., Gasevic, D., Dawson, S., & Pardo, A. (2019). Complexity leadership in learning analytics: Drivers, challenges and opportunities. British Journal of Educational Technology, 50, 2839-2854. https://doi.org/10.1111/bjet.12846

Tusquellas, N., Palau, R., & Santiago, R. (2024). Analysis of the potential of artificial intelligence for professional development and talent management: A systematic literature review. International Journal of Information Management Data Insights, 4(2), 100288. https://doi.org/10.1016/j.jjimei.2024.100288

Uttamchandani, S., & Quick, J. (2022). An introduction to fairness, absence of bias, and equity in learning analytics. In C. Lang, G. Siemens, A. F. Wise, D. Gaševic, ´ & A. Merceron (Eds.), The handbook of learning analytics (2 ed.) (pp. 205–212). SoLAR. https://doi.org/10.18608/hla22.020

Viberg, O., Mutimukwe, C., & Grönlund, A. (2022). Privacy in la research: Understanding the field to improve the practice. Journal of Learning Analytics, 9, 169–182. https://doi.org/10.18608/jla.2022.7751

Viktorelius, M., & Larsson, S. (2025). Talking about machines: discursive sensemaking and workplace learning in the age of AI. Studies in Continuing Education, 1-19. https://doi.org/10.1080/0158037X.2025.2570144

Wang, C., & Xiao, A. (2025). Anxiety induced by artificial intelligence (ai) painting: an investigation based on the fear acquisition theory. Psychol Trauma Theory Res Pract Policy, 17(1), 214-224. https://doi.org/10.1037/tra000186

Wang, C., Zhu, M., & Zakaria, S. A. S. (2025). Cross-modal deep learning enhanced mixed reality accelerates construction skill transfer from experts to students. Scientific Reports, 15(1), 34462. https://doi.org/10.1038/s41598-025-17656-0

Wang, J., Kiran, E., Aurora, S. R., Simeone, M., & Lobo, J. (2025). ChatGPT on ChatGPT: An exploratory analysis of its performance in the public sector workplace. Digital Government: Research and Practice, 6(2), 1-28. https://doi.org/10.1145/3676281

Wang, S., Hu, T., Xiao, H., Li, Y., Zhang, C., Ning, H., Zhu, R., Li, Z., & Ye, X. (2024). GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review. International Journal of Digital Earth, 17(1). https://doi.org/10.1080/17538947.2024.2353122

Watanabe, Y., Nakayama, M., Takemura, K., & Uchida, Y. (2025). AI feedback and workplace social support in enhancing occupational self-efficacy: a randomized controlled trial in Japan. Scientific Reports, 15(1), 11301. https://doi.org/10.1038/s41598-025-94985-0

Whale, A., & Scholtz, B. (2024). An Architecture for Workplace Learning Analytics (WLA) to Support Lifelong Learning in Sustainable Smart Organisations. Sustainability, 16(9), 3595. https://doi.org/10.3390/su16093595

Xavier, D. F., Korunka, C., & Reiter-Palmon, R. (2025). AI integration and workforce development: Exploring job autonomy and creative self-efficacy in a global context. PLoS One, 20(6), e0319556. https://doi.org/10.1371/journal.pone.0319556

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