Adult learners often prefer asynchronous online learning because of its flexibility and accessibility. However, designing effective asynchronous online courses requires instructors to address the diverse needs of adult learners. Guided by the Universal Design for Learning (UDL) and Knowles’ four principles of andragogy, this innovation in practice presents several AI-assisted learning strategies, such as adaptive quizzes, chatbots, and virtual tutors that may directly enhance the design of asynchronous online courses by personalizing pathways, increasing engagement, and supporting self-directed learning. It is expected that this piece will offer practical implications for adult educators seeking to integrate artificial intelligence (AI) into their online teaching while enhancing accessibility, learner engagement, and overall instructional effectiveness.
Abou-KhalilV.HollensteinL.FerraioliV.VogtM.FlanaganB.VogtF.MondadaF. (2025). Preparing teachers for an equitable AI integration in education. In CristeaA. I.WalkerE.LuY.SantosO. C.IsotaniS. (Eds.), Artificial intelligence in education: Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners, doctoral consortium, blue sky, and WideAIED, communications in computer and information science (2592, pp. 249–254). Springer. https://doi.org/10.1007/978-3-031-99267-4_31
2.
AhmedF.FatimaN.SardarM. B.AlibhaiM. (2024). Exploring the integration of virtual assistant using large language models in learning management system: Enhancing educational accessibility and efficiency. In 2024 international conference on information technology systems and innovation (ICITSI) (pp. 189–198). IEEE. https://doi.org/10.1109/ICITSI65188.2024.10929366
3.
AkpenC. N.AsaoluS.AtobateleS.OkagbueH.SampsonS. (2024). Impact of online learning on student's performance and engagement: A systematic review. Discover Education, 3(1), 205. https://doi.org/10.1007/s44217-024-00253-0
4.
AlshammariS. H.AlrehailiT. A. (2025). The effect of teaching, social, and cognitive presence on student engagement in online courses: A structural equation modelling approach. Acta Psychologica, 258(1), Article 105183. https://doi.org/10.1016/j.actpsy.2025.105183
5.
BaliS.ChenT. C.LiuM. C.KlangritS.LinC. Y. (2024). Navigating interactions and challenges in online learning: A qualitative study through social presence theory. Qualitative Research Journal. https://doi.org/10.1108/QRJ-04-2024-0077
6.
BanikB. G.GullapellyA. (2025). AI-powered gamification and interactive learning tools for enhancing student engagement. In Driving quality education through AI and data science (pp. 283–310). IGI Global. https://doi.org/10.4018/979-8-3693-8292-9.ch013
7.
BeckmanK.AppsT.HowardS. K.RogersonC.RogersonA.TondeurJ. (2025). The GenAI divide among university students: A call for action. The Internet and Higher Education, 67(2), Article 101036. https://doi.org/10.1016/j.iheduc.2025.101036
8.
CainM.SheehanH.TaoukS. (2024). “It doesn't feel like we've had the chance to really connect”. The crucial need for social presence in fully asynchronous teacher education. Teaching and Teacher Education, 152(3), Article 104789. https://doi.org/10.1016/j.tate.2024.104789
9.
CAST. (2011). Universal design for learning guidelines version 2.0. Wakefield.
10.
CAST. (2015). Universal design for learning: Theory and practice. Wakefield.
11.
CoureyS. J.TappeP.SikerJ.LePageP. (2013). Improved lesson planning with universal design for learning (UDL). Teacher Education and Special Education, 36(1), 7–27. https://doi.org/10.1177/0888406412446178
12.
EngenessI.NohrM.FosslandT. (2025). Investigating AI chatbots’ role in online learning and digital agency development. Education Sciences, 15(6), 674. https://doi.org/10.3390/educsci15060674
13.
García-TorresD.FernándezC.MiraJ. J.MoralesA.VicenteM. A. (2025). Using AI-Based virtual simulated patients for training in psychopathological interviewing: Cross-sectional observational study. JMIR Medical Education, 11, Article e78857. https://doi.org/10.2196/78857
14.
GarrisonD. R.AndersonT.ArcherW. (1999). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6
15.
HanJ.DiGiacomoD. K.UsherE. L. (2023). College students’ self-regulation in asynchronous online courses during COVID-19. Studies in Higher Education, 48(9), 1440–1454. https://doi.org/10.1080/03075079.2023.2201608
16.
HuT.KyrychenkoY.RathjeS.CollierN.Van Der LindenS.RoozenbeekJ. (2025). Generative language models exhibit social identity biases. Nature Computational Science, 5(1), 65–75. https://doi.org/10.1038/s43588-024-00741-1
17.
KajiwaraY.MatsuokaA.ShinboF. (2023). Machine learning role playing game: Instructional design of AI education for age-appropriate in K-12 and beyond. Computers and Education: Artificial Intelligence, 5(3), Article 100162. https://doi.org/10.1016/j.caeai.2023.100162
18.
KangH. (2023). Artificial intelligence and its influence in adult learning in China. Higher Education, Skills and Work-based Learning, 13(3), 450–464. https://doi.org/10.1108/HESWBL-01-2023-0017
19.
KearsleyG. (2010). Andragogy (M. Knowles). The theory into practice database. https://tip.psychology.org
20.
KimJ.LeeH.ChoY. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
21.
KimJ.YuS.DetrickR.LinX.LiN. (2025). Designing AI-powered learning: Adult learners’ expectations for curriculum and human-AI interaction. Educational Technology Research & Development, 73(6), 3397–3421. https://doi.org/10.1007/s11423-025-10549-z
22.
KnowlesM. S. (1980). The modern practice of adult education: From pedagogy to andragogy (2). Cambridge Adult Education.
23.
KnowlesM. S. (1984a). Andragogy in action: Applying modern principles of adult learning. Jossey-Bass.
24.
KnowlesM. S. (1984b). The adult learner: A neglected species (3rd ed.). Gulf Publishing.
LavanyaB.MadhuriJ. N.PagidipatiB.MythiliM.ThiyagarajanK.PremaS. (2024). Adaptive testing with machine learning: Customizing English proficiency assessments based on learner performance. In 2024 international conference on communication, control, and intelligent systems (CCIS) (pp. 1–5). IEEE. https://doi.org/10.1109/CCIS63231.2024.10931968
27.
LeeG.ShiL.LatifE.GaoY.BewersdorffA.NyaabaM.GuoS.WuZ.LiuZ.WangH.MaiG.LiuT.ZhaiX. (2025). Multimodality of AI for education: Towards artificial general intelligence. IEEE Transactions on Learning Technologies, 18, 666–683. https://doi.org/10.1109/TLT.2025.3574466
28.
LewitzkyR. A.WeaverK. D. (2022). Developing universal design for learning asynchronous training in an academic library. Partnership, 16(2), 1–18. https://doi.org/10.21083/partnership.v16i2.6635
29.
LinX. (2024). Exploring the role of ChatGPT as a facilitator for motivating self-directed learning among adult learners. Adult Learning, 35(3), 156–166. https://doi.org/10.1177/10451595231184928
30.
LinX. (2025). A preliminary study investigating adult learners’ perspectives of using ChatGPT to seek effective assignment feedback. Australian Educational Researcher, 52(5), 3393–3420. https://doi.org/10.1007/s13384-025-00856-y
31.
LinX.GaoL. (2020). Students’ sense of community and perspectives of taking synchronous and asynchronous online courses. Asian Journal of Distance Education, 15(1), 169–179.
32.
LinX.ZhaoT.SchmidtS. W.ZhouS. (2025). Using AI as a learning tool through simulation interviews to enhance adult learning. Adult Learning, 10451595251345274. https://doi.org/10.1177/10451595251345274
33.
MansourN. (2024). Students’ and facilitators’ experiences with synchronous and asynchronous online dialogic discussions and e-facilitation in understanding the nature of science. Education and Information Technologies, 29(12), 15965–15997. https://doi.org/10.1007/s10639-024-12473-w
34.
ManuelC. C. J. (2025). The impact of AI on inclusivity in higher education: A rapid review. Education Sciences, 15(9), 1255. https://doi.org/10.3390/educsci15091255
35.
MarínY. R.CaroO. C.RituayA. M. C.LlanosK. A. G.PerezD. T.BardalesSantosE. S. R. C. (2025). Ethical challenges associated with the use of artificial intelligence in university education. Journal of Academic Ethics, 23(4), 2443–2467. https://doi.org/10.1007/s10805-025-09660-w
PesovskiI.SantosR.HenriquesR.TrajkovikV. (2024). Generative AI for customizable learning experiences. Sustainability, 16(7), 3034. https://doi.org/10.3390/su16073034
38.
PettyT.ThomasC. (2014). Approaches to a successful adult education program. College Student Journal, 48(3), 473–480.
39.
ResnikP. (2025). Large language models are biased because they are large language models. Computational Linguistics, 51(3), 885–906. https://doi.org/10.1162/coli_a_00558
40.
RitterL. A. (2026). The impact of AI tools on adult learning outcomes in online education. International Journal of Research and Review, 13(2), 132–140. https://doi.org/10.52403/ijrr.20260213
41.
Rogers-ShawC.Carr-ChellmanD. J.ChoiJ. (2018). Universal design for learning: Guidelines for accessible online instruction. Adult Learning, 29(1), 20–31. https://doi.org/10.1177/1045159517735530
42.
RoseD. H.HarbourW. S.JohnstonC. S.DaleyS. G.AbarbanellL. (2006). Universal design for learning in postsecondary education: Reflections on principles and their application. Journal of Postsecondary Education and Disability, 19(2), 135–151. https://www.learntechlib.org/p/77561/
43.
RoseD. H.MeyerA.HitchcockC. (2005). The universally designed classroom: Accessible curriculum and digital technologies. Harvard Education Press.
44.
SteleaG. A.RobuD.SanduF. (2025). Accessilearnai: An accessibility-first, ai-powered e-learning platform for inclusive education. Education Sciences, 15(9), 1125. https://doi.org/10.3390/educsci15091125
SturgesM.GrayT.GalbraithC. (2025). Theorising universal design for learning to create more inclusive outdoor play spaces: A preliminary review. Education Sciences, 15(12), 1623. https://doi.org/10.3390/educsci15121623
47.
TanL. Y.HuS.YeoD. J.CheongK. H. (2025). Artificial intelligence-enabled adaptive learning platforms: A review. Computers and Education: Artificial Intelligence, 9, Article 100429. https://doi.org/10.1016/j.caeai.2025.100429
48.
TekirS. (2025). Strategies for effective classroom management in online teaching: A post-pandemic review of empirical studies. Sage Open, 15(3), 21582440251377321. https://doi.org/10.1177/21582440251377321
49.
Veytia BucheliM. G.Gómez-GalánJ.Cáceres MesaM. L.López CatalánL. (2024). Digital technologies as enablers of universal design for learning: Higher education students' perceptions in the context of SDG4. Discover Sustainability, 5(1), 473. https://doi.org/10.1007/s43621-024-00699-0
50.
WangQ.CamachoI.JingS.GoelA. 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. https://doi.org/10.1145/3512977
51.
WangT.WuT.LiuH.BrownC.ChenY. (2025). Generative co-learners: Enhancing cognitive and social presence of students in asynchronous learning with generative AI. Proceedings of the ACM on Human-Computer Interaction, 9(1), 1–24. https://doi.org/10.1145/370119
52.
WangX.WredeS.van RijnL.WöhrleJ. (2023). AI-based quiz system for personalised learning. In ICERI2023 proceedings (pp. 5025–5034). IATED. https://doi.org/10.21125/iceri.2023.1257
53.
WuX. Y.ChiuT. K. (2025). Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: A configurational analysis. Journal of New Approaches in Educational Research, 14(1), 10. https://doi.org/10.1007/s44322-025-00028-x
54.
WuX. Y.RadloffJ. D.YeterI. H.WangL.ChiuT. K. (2025). Designing artificial intelligence chatbots for self-regulated learning from a systematic review based on Habermas’s three interests. Interactive Learning Environments, 1–24. https://doi.org/10.1080/10494820.2025.2563086
55.
YuE. (2020). Student-inspired optimal design of online learning for generation Z. Journal of Educators Online, 17(1), Article 1. https://doi.org/10.9743/jeo.2023.20.1.16
56.
ZhaiC.WibowoS.LiL. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart learning environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
57.
ZhuangM.LongS.MartinF.Castellanos-ReyesD. (2025). The affordances of artificial intelligence (AI) and ethical considerations across the instruction cycle: A systematic review of AI in online higher education. The Internet and Higher Education, 67(2), Article 101039. https://doi.org/10.1016/j.iheduc.2025.101039
58.
ZsifkovitsM.AmplatzL.TriebnerN.UtzJ.KornhuberJ.SpitzerP. (2025). Randomized controlled trial of asynchronous vs. synchronous online teaching formats: Equal knowledge after training, greater acceptance and lower intrinsic motivation through asynchronous online learning. BMC Medical Education, 25(1), 850. https://doi.org/10.1186/s12909-025-07481-4
