Scalable Community Outreach for AI Literacy and Future Ready Skills Evidence from Indonesia–Malaysia Collaboration
DOI:
https://doi.org/10.51601/ijcs.v6i1.963Abstract
This study aimed to strengthen students’ and the general public’s understanding of how artificial intelligence (AI) is reshaping work and to promote future-ready competencies, including strategic upskilling and reskilling. The community-service activity was delivered in Indonesia via a Zoom Meeting webinar on 28 November 2025 titled “Shaping the Future Workforce in the Age of Artificial Intelligence.” Content was prepared from key reports on AI and work. Participant registration and feedback were collected using Google Forms (Google LLC). The intervention included expert talks (industry practitioner and academic), moderated discussion, and Q&A. Evaluation used a post-webinar survey with 5 point Likert items and open-ended questions. Participants reported clearer awareness of AI-driven job changes, emerging roles, and the importance of data/AI literacy. Key themes were continuous learning, building practical portfolios, and responsible AI use (bias, privacy, and validating AI outputs). The findings rely on self-reported perceptions from a single webinar, without a pre–post test, objective skill measures, or long-term follow-up. This study offers a replicable, low-cost webinar model for workforce-readiness education and contributes to HR development and digital-talent training by translating AI trends into actionable, practical competency guidance for higher education and community partners.
Downloads
References
[1] World Economic Forum. The Future of Jobs Report 2023 [Internet]. Geneva: World Economic Forum; 2023 [cited 2026 Jan 15]. Available from: https://www.weforum.org/reports/the-future-of-jobs-report-2023/ (ictlogy.net)
[2] Jarrahi MH. Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Bus Horiz. Elsevier 2018;61(4):577-86. doi:10.1016/j.bushor.2018.03.007
[3] Marler JH, Boudreau JW. An evidence-based review of HR Analytics. Int J Hum Resour Manag. 2017;28(1):3-26. https://doi.org/10.1080/09585192.2016.1244699
[4] Davenport TH, Harris JG. Competing on Analytics: The New Science of Winning. Boston (MA): Harvard Business School Press; 2007. https://www.researchgate.net/publication/7327312_Competing_on_Analytics
[5] Sun PC, Tsai RJ, Finger G, Chen YY, Yeh D. What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput Educ. 2008;50(4):1183-202. doi: https://doi.org/10.1016/j.compedu.2006.11.007
[6] Means B, Toyama Y, Murphy R, Bakia M. The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teach Coll Rec. 2013;115(3):1-47. doi: 10.1177/016146811311500307
[7] Gegenfurtner A, Ebner C. Webinars in higher education and professional training: A meta-analysis and systematic review of randomized controlled trials. Educ Res Rev. 2019;28:100293. doi: 10.1016/j.edurev.2019.100293
[8] Autor DH. Why are there still so many jobs? The history and future of workplace automation. J Econ Perspect. 2015;29(3):3-30. doi: 10.1257/jep.29.3.3
[9] Brynjolfsson E, McAfee A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton & Company; 2014. Doi; 10.1080/14697688.2014.946440
[10] Organisation for Economic Co-operation and Development (OECD). OECD Skills Outlook 2019: Thriving in a Digital World [Internet]. Paris: OECD Publishing; 2019 [cited 2026 Jan 15]. Available from: https://www.oecd.org/education/oecd-skills-outlook-2019-df80bc12-en.htm (OECD)
[11] Bessen J. Automation and jobs: when technology boosts employment. Econ Policy. 2019;34(100):589-626. https://doi.org/10.1093/epolic/eiaa001
[12] Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1:389-99. doi: 10.1038/s42256-019-0088-2
[13] Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, et al. AI4People—An ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Mach. 2018;28(4):689-707. doi: 10.1007/s11023-018-9482-5
[14] Nonaka I. A dynamic theory of organizational knowledge creation. Organ Sci. 1994;5(1):14-37. http://dx.doi.org/10.1287/orsc.5.1.14
[15] Inkpen AC, Tsang EW. Social capital, networks, and knowledge transfer. Acad Manage Rev. 2005;30(1):146-65. https://doi.org/10.5465/amr.2005.15281445
[16] Raisch S, Krakowski S. Artificial intelligence and management: The automation–augmentation paradox. Acad Manage Rev. 2021;46(1):192-210. https://doi.org/10.5465/amr.2018.0072 .
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Agung Pramayuda, Mugi Puspita, Irfan Achmad Musadat, Agung Pramayuda, Immanuel David Martsinus Situmorang
This work is licensed under a Creative Commons Attribution 4.0 International License.
