International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 976KB)
Insan AI: Integrated Artificial Intelligence Learning Platform
PDF (976KB), PP.145-158
Views: 0 Downloads: 0
Author(s)
Winanti
1,*
Yoga Prihastomo
1
Yulius Denny Prabowo
2
Achmad Sidik
3
Penny Hendriyati
4
Index Terms
Learning Platform, Artificial Intelligence, AI Solution, Prototyping Model, Generative AI
Abstract
The integration of artificial intelligence (AI) in education presents significant challenges, such as gaps in educators' digital adaptability, ethical considerations, and inconsistent infrastructure. This study details the development and validation of the Insan AI Platform, an integrated learning solution designed to address these obstacles through adaptive AI tools for both teachers and students. The platform was developed using a user-centered prototyping methodology, drawing on comprehensive literature analysis, Focus Group Discussions with 26 educational stakeholders, and expert interviews. Key features include an AI Content Generator, a Virtual Tutor, and a Learning Analytics dashboard, all intended to facilitate personalized learning experiences and enhance teaching efficiency. User Acceptance Testing with 20 teachers demonstrated the platform's functional robustness, with perfect pass rates on all core features and a high usability score (SUS: 84.0). The platform architecture integrates multiple AI application programming interfaces (APIs) while maintaining responsive performance under varied network conditions. These findings indicate that the Insan AI Platform effectively meets user requirements and provides a strong foundation for broader educational implementation. Future development will focus on incorporating multilingual support and advanced learning analytics capabilities. According to a questionnaire completed by 26 users, there was a score increase of 22.42 after using the Insan AI platform. This indicates that the application has successfully met user requirements
Cite This Paper
Winanti, Yoga Prihastomo, Yulius Denny Prabowo, Achmad Sidik, Penny Hendriyati, "Insan AI: Integrated Artificial Intelligence Learning Platform", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.1, pp. 145-158, 2026. DOI:10.5815/ijieeb.2026.01.09
Reference
[1]T. Matos, W. Santos, E. Zdravevski, P. J. Coelho, I. M. Pires, and F. Madeira, “A systematic review of artificial intelligence applications in education: Emerging trends and challenges,” Decis. Anal. J., vol. 15, no. April, p. 100571, Jun. 2025, doi: 10.1016/j.dajour.2025.100571.
[2]W. Winanti, S. Basuki, and N. Nurasiah, “A Systematic Literature Review : Impact of Generative AI as Technology to Learning in Higher Education,” KHAZANAH Inf., vol. 10, no. 2, 2025.
[3]Y. Dai, A. Liu, and C. P. Lim, “Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education,” Procedia CIRP, vol. 119, pp. 84–90, 2023, doi: 10.1016/j.procir.2023.05.002.
[4]C. Stechert and A. Bode, “Agile Methods in Teaching Digital Engineering,” Procedia CIRP, vol. 136, pp. 689–694, 2025, doi: 10.1016/j.procir.2025.08.118.
[5]Å. Cajander, M. Larusdottir, and J. L. Geiser, “UX professionals’ learning and usage of UX methods in agile,” Inf. Softw. Technol., vol. 151, no. November 2021, p. 107005, Nov. 2022, doi: 10.1016/j.infsof.2022.107005.
[6]A. Y. Q. Huang, O. H. T. Lu, and S. J. H. Yang, “Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom,” Comput. Educ., vol. 194, p. 104684, Mar. 2023, doi: 10.1016/j.compedu.2022.104684.
[7]E. Ukwandu, O. Omisade, K. Jones, S. Thorne, and M. Castle, “The future of teaching and learning in the context of emerging artificial intelligence technologies,” Futures, vol. 171, no. May 2024, p. 103616, Aug. 2025, doi: 10.1016/j.futures.2025.103616.
[8]S. Salih, O. Husain, M. Hamdan, S. Abdelsalam, H. Elshafie, and A. Motwakel, “Transforming education with AI: A systematic review of ChatGPT’s role in learning, academic practices, and institutional adoption,” Results Eng., vol. 25, no. December 2024, p. 103837, Mar. 2025, doi: 10.1016/j.rineng.2024.103837.
[9]A. K. Khoso, W. Honggang, and M. A. Darazi, “Empowering creativity and engagement: The impact of generative artificial intelligence usage on Chines EFL students’ language learning experience,” Comput. Hum. Behav. Reports, vol. 18, no. November 2024, p. 100627, May 2025, doi: 10.1016/j.chbr.2025.100627.
[10]K. Yang et al., “The Engagement of Prospective Chinese Engineers in Translation Software and Generative AI toward Learning English,” Procedia Comput. Sci., vol. 257, pp. 23–30, 2025, doi: 10.1016/j.procs.2025.03.006.
[11]O. Embarak, “A Behaviour-Driven Framework for Smart Education: Leveraging Explainable AI and IoB in Personalized Learning Systems,” Procedia Comput. Sci., vol. 265, pp. 457–466, 2025, doi: 10.1016/j.procs.2025.07.205.
[12]M. Smit, T. Bond-Barnard, and R. F. Wagner, “Artificial intelligence in South African higher education: Survey data of master’s level students,” Data Br., vol. 61, p. 111813, Aug. 2025, doi: 10.1016/j.dib.2025.111813.
[13]M. Lünich, B. Keller, and F. Marcinkowski, “Diverging perceptions of artificial intelligence in higher education: A comparison of student and public assessments on risks and damages of academic performance prediction in Germany,” Comput. Educ. Artif. Intell., vol. 7, no. August, p. 100305, Dec. 2024, doi: 10.1016/j.caeai.2024.100305.
[14]A. O. (Olnancy) Tzirides et al., “Combining human and artificial intelligence for enhanced AI literacy in higher education,” Comput. Educ. Open, vol. 6, no. May, p. 100184, Jun. 2024, doi: 10.1016/j.caeo.2024.100184.
[15]I. Ernawati, N. M. Yasin, I. Setyopranoto, and Z. Ikawati, “Development of a mobile health application for epilepsy self-management: Focus group discussion and validity of study results,” Clin. eHealth, vol. 7, no. 2024, pp. 190–199, Dec. 2024, doi: 10.1016/j.ceh.2024.12.005.
[16]D. B. Olawade, D. Omeni, M. N. Gore, and M. Hadi, “Enhancing qualitative research through virtual focus groups and artificial intelligence: A review,” Int. J. Med. Inform., vol. 203, no. May, p. 106004, Nov. 2025, doi: 10.1016/j.ijmedinf.2025.106004.
[17]N. Hachoumi, M. Eddabbah, and A. R. El adib, “Enhancing teaching and learning in health sciences education through the integration of Bloom’s taxonomy and artificial intelligence,” Informatics Heal., vol. 2, no. 2, pp. 130–136, 2025, doi: 10.1016/j.infoh.2025.05.002.
[18]K. F. Nzembayie and D. Urbano, “Generative AI platforms as institutional catalysts of digital entrepreneurship: Enablement, dependence & power dynamics,” Technol. Soc., vol. 84, no. September 2025, p. 103074, 2026, doi: 10.1016/j.techsoc.2025.103074.
[19]G. Li and T. Tang, “Online performance and interface design implications among older adults: A systematic review of eye tracking studies,” Appl. Ergon., vol. 128, no. April, p. 104538, 2025, doi: 10.1016/j.apergo.2025.104538.
[20]P. Soni, C. de Runz, F. Bouali, and G. Venturini, “A survey on automatic dashboard recommendation systems,” Vis. Informatics, vol. 8, no. 1, pp. 67–79, 2024, doi: 10.1016/j.visinf.2024.01.002.
[21]D. L. Mann, “Artificial Intelligence Discusses the Role of Artificial Intelligence in Translational Medicine: A JACC: Basic to Translational Science Interview With ChatGPT,” JACC Basic to Transl. Sci., vol. 8, no. 2, pp. 221–223, 2023, doi: 10.1016/j.jacbts.2023.01.001.
[22]V. Dakulagi, K. H. Yeap, H. Nisar, R. Dakulagi, G. N. Basavaraj, and M. V. Galindo, “An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces,” in Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction, Elsevier, 2025, pp. 63–77. doi: 10.1016/B978-0-443-29150-0.00002-0.
[23]H. Khosravi et al., “Explainable Artificial Intelligence in education,” Comput. Educ. Artif. Intell., vol. 3, no. April, 2022, doi: 10.1016/j.caeai.2022.100074.
[24]L. Razanakolona, J. P. Razafimandimby, A. H. C. C. Andriamiandanomenajanahry, and Y. Rhazali, “Approach Model Driven Engineering: Profiling Group Collaborative Learner in Mobile Learning,” Procedia Comput. Sci., vol. 170, pp. 863–868, 2020, doi: 10.1016/j.procs.2020.03.143.
[25]S. A. Sualim, N. M. Yassin, and R. Mohamad, “Comparative Evaluation of Automated User Acceptance Testing Tool for Web Based Application Sherolwendy,” Int. J. Softw. Eng. Technol., vol. 02, no. 2, pp. 1–6, 2016.