The Use of Generative AI to Support Inclusivity and Design Deliberation for Online Instruction
DOI:
https://doi.org/10.24059/olj.v28i3.4458Keywords:
Design deliberations, ethical analysis, localization of context, instructional design, decision-makingAbstract
Generative AI presents significant opportunities for instructional designers to revolutionize content creation and personalization in online learning environments. This paper explores how generative AI can streamline content generation processes, enhance adaptability to individual learner needs, and improve feedback mechanisms, ultimately fostering more engaging and inclusive learning experiences. Alongside its benefits, generative AI also poses ethical considerations and potential risks, such as perpetuating biases or disrupting the learning process. Navigating these complexities requires a deliberate approach to design deliberation, that involves careful analysis, discussion, and decision-making throughout the design process. This paper proposes a conceptual framework to support instructional designers in leveraging generative AI to promote inclusivity within their design deliberations, emphasizing the importance of addressing ethical considerations and engaging in iterative design practices.
References
Baaki, J., & Tracey, M.W. (2022). Empathy for action in instructional design. In J.E. Stefaniak
& R.M. Reese (Eds.), The instructional design trainer’s guide: Authentic practices and
considerations for mentoring ID and ed tech professionals (pp. 56-66). Routledge.
Baaki, J., Tracey, M. W., & Hutchinson, A. (2017). Give us something to react to and make it
rich: Designers reflecting-in-action with external representations. International Journal
of Technology and Design Education, 27, 667-682.
https://doi.org/10.1007/s10798-016-9371-2
Barnum, M. (2024, Feb. 16). We tested an AI tutor for kids. It struggled with basic math. The
Wall Street Journal. https://www.wsj.com/tech/ai/ai-is-tutoring-students-but-still-struggles-with-basic-math-694e76d3
Boling, E., Alangari, H., Hajdu, I. M., Guo, M., Gyabak, K., Khlaif, Z., Kizilboga, R., Tomita,
K., Alsaif, M., Lachheb, A., Bae, H., Ergulec, F., Zhu, M., Basgodan, M., Buggs, C.,
Sari, A., & Techawitthayachinda, R. I. (2017). Core judgments of instructional designers
in practice. Performance Improvement Quarterly, 30(3), 199-219.
https://doi.org/10.1002/piq.21250
Boling, E., & Gray, C. M. (2015). Designerly tools, sketching, and instructional designers and
the guarantors of design. In B. Hokanson, G. Clinton, & M.W. Tracey (Eds.), The design
of learning experience: Creating the future of educational technology (pp. 109-126).
Springer.
Boling, E., & Gray, C. M. (2018). Use of precedent as a narrative practice in design
learning. Educational Technology and Narrative: In B. Hokanson, G. Clinton, & K.
Kaminski (Eds.), Story and Instructional Design (pp. 259-270). Springer.
Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational
agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1), 198-
204. https://orcid.org/0000-0002-4520-642X
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond,
M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E.,
Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L.,
Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J.
E., Santos-Hermosa, G Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R.,
C., Hrastinski, S., & Jandrić, P. (2023). Speculative futures on ChatGPT and generative
artificial intelligence (AI): A collective reflection from the educational landscape. Asian
Journal of Distance Education, 18(1), 53-130. https://doi.org/10.5281/zenodo.7636568
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and
challenges in higher education. International Journal of Educational Technology in
Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Chang, D. H., Lin, M. P. C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921.
https://doi.org/10.3390/su151712921
Cook-Sather, A. (2003). Movements of mind: The Matrix, metaphors, and re-imagining
education. Teachers College Record, 105(6), 946–977.
https://doi.org/10.1111/1467-9620.00274
Firat, M. (2023). Integrating AI applications into learning management systems to enhance
e-learning. Instructional Technology and Lifelong Learning, 4(1), 1–14.
https://doi.org/10.52911/itall.1244453
Garrison, D. R., Anderson, T., & Archer, W. (2000). 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
Gibson, J.J. (1979). The ecological approach to visual perception. Houghton Mifflin.
Glahn, C., & Gruber, M. R. (2020). Designing for context-aware and contextualized learning. In
S. Yu, M. Ally, & A. Tsinakos (Eds.), Emerging technologies and pedagogies in the
curriculum, (pp. 21-40). Springer.
Gunawardena, C. F. C., Frechette, C., & Layne, L. (2018). Culturally inclusive instructional
design. Routledge.
Gurjar, N., & Bai, H. (2023). Assessing culturally inclusive instructional design in online
learning. Educational Technology Research and Development, 71(3), 1253-1274.
https://doi.org/10.1007/s11423-023-10226-z
Hassan, S., Huenerfauth, M., & Alm, C. O. (2021). Unpacking the interdependent systems of
discrimination: Ableist bias in NLP systems through an intersectional lens. Findings of
the Association for Computational Linguistics: EMNLP 2021, 3116-3123.
Henneborn, L. (2023, Aug. 18). Designing generative AI to work for people with disabilities.
Harvard Business Review. https://hbr.org/2023/08/designing-generative-ai-to-work-for-people-with-disabilities
Herman, K., Baaki, J., & Tracey, M. W. (2023). “Faced with given circumstances”: A localized
context of use approach. In B. Hokanson, M. Exter, M.M. Schmidt, & A.A. Tawfik
(Eds.), Toward inclusive learning design: Social justice, equity, and community (pp. 397-
407). Springer.
Hodges, C. B., & Kirschner, P. A. (2024). Innovation of instructional design and assessment in
the age of generative artificial intelligence. TechTrends, 68(1), 195-199.
https://doi.org/10.1007/s11528-023-00926-x
Hsu, Y. C., & Ching, Y. H. (2023). Generative artificial intelligence in education, Part one: The
dynamic frontier. TechTrends, 1-5. https://doi.org/10.1007/s11528-023-00863-9
Kadaruddin, K. (2023). Empowering education through generative AI: Innovative instructional
strategies for tomorrow's learners. International Journal of Business, Law, and
Education, 4(2), 618-625. https://doi.org/10.56442/ijble.v4i2.215
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education,
106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning.
RELC Journal, 54(2). https://doi.org/10.1177/00336882231162868
Könings, K. D., Brand-Gruwel, S. & van Merriënboer, J. J. (2011). The match between students’
lesson perceptions and preferences: Relations with student characteristics and the
importance of motivation. Educational Research, 53(4), 439–457.
https://doi.org/10.1080/00131881.2011.625155
Könings, K. D., Brand-Gruwel, S. & Merriënboer, J. J. G. (2005). Towards more powerful
learning environments through combining the perspectives of designers, teachers, and
students. British Journal of Educational Psychology, 75(4), 645–660.
https://doi.org/10.1348/000709905x43616
Könings, K. D., Seidel, T. & van Merriënboer, J. J. G. (2014). Participatory design of learning
environments: Integrating perspectives of students, teachers, and designers. Instructional
Science, 42(1), 1–9. https://doi.org/10.1007/s11251-013-9305-2
Kuhail, M. A., Alturki, N., Alramlawi, S., et al. (2023). Interacting with educational chatbots: A
systematic review. Education and Information Technology, 28, 973–1018.
https://doi.org/10.1007/s10639-022-11177-3
Lomellini, A., Reese, R. M., & Grennell, K. (2023). The Imperfection of accessibility in
instructional design: An ethical dilemma. In S. L. Moore & T. A. Dousay (Eds.),
Applied ethics for instructional design and technology: Design, decision making, and
contemporary issues. EdTech Books.
https://edtechbooks.org/applied_ethics_idt/the_imperfection_of_accessibility_in_instructional_design
McDonald, J.K. (2022). Preparing instructional designer students for reflective practice. In J.E.
Stefaniak & R.M. Reese (Eds.), The instructional design trainer’s guide: Authentic
practices and considerations for mentoring ID and ed tech professionals (pp. 29-37).
Routledge.
McDonald, J. K. (2023). The everydayness of instructional design and the pursuit of quality in
online courses. Online Learning, 27(2), 137-169. https://doi.org/10.24059/olj.v27i2.3470
Moore, M.G. (1989). Editorial: Three types of interaction. The American Journal of Distance
Education, 3(2), 1-7. https://doi.org/10.1080/08923648909526659
Moore, M.G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical
principles of distance education (pp. 22-38). Routledge.
Moore, S., Hedayati-Mehdiabadi, A., Kang, P., & Law, V. (2024). The change we work:
Agency and ethics in emerging AI technologies. TechTrend, 68(1),27-36.
https://doi.org/10.1007/s11528-023-00895-1
Moore, S.L. & Tillberg-Webb, H. (2023). Ethics in educational technology: Reflection
interrogation and design as a framework for practice. Routledge.
https://doi.org/10.4324/9780203075241
Penuel, W. R., Allen, A. R., Henson, K., Campanella, M., Patton, R., Rademaker, K., ... & Zivic,
A. (2022). Learning practical design knowledge through co-designing storyline science
curriculum units. Cognition and Instruction, 40(1), 148-170.
https://doi.org/10.1080/07370008.2021.2010207
Rao, K. (2021). Inclusive instructional design: Applying UDL to online learning. The Journal of
Applied Instructional Design, 10(1), 83-97. https://doi.org/10.59668/223.3753
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key
challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-
Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003
Rieber, L.P., & Estes, M. (2017). Accessibility and instructional technology: Reframing the
discussion. Journal of Applied Instructional Design, 6(1), 9-19.
https://doi.org/10.28990/jaid2017.061001
Riener, C. & Willingham, D. (2010). The myth of learning styles. Change: The Magazine of
Higher Learning, 42(5), 32–35.
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023).
Empowering education with generative artificial intelligence tools: Approach with an
instructional design matrix. Sustainability, 15(15), 11524.
https://doi.org/10.3390/su151511524
Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024).
Using generative artificial intelligence tools to explain and enhance experiential
learning for authentic assessment. Education Sciences, 14(1), 83.
https://doi.org/10.3390/educsci14010083
Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.
Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying
generative AI in education. ECNU Review of Education, 6(3), 355-
366. https://doi.org/10.1177/2096531123116842
Svihla, V. & Kachelmeier, L. (2020). The Wrong Theory Protocol: A design thinking tool to
enhance creative ideation. Proceedings of the Sixth International Conference on Design
Creativity (ICDC 2020), pp. 223-230. https://doi.org/10.35199/ICDC.2020.28
Thanh, B. N., Vo, D. T. H., Nhat, M. N., Pham, T. T. T., Trung, H. T., & Xuan, S. H. (2023).
Race with the machines: Assessing the capability of generative AI in solving authentic
assessments. Australasian Journal of Educational Technology, 39(5), 59-81.
https://doi.org/10.14742/ajet.8902
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B.
(2023). What if the devil is my guardian angel: ChatGPT as a case study of using
chatbots in education. Smart Learning Environments, 10(1), 1-24.
https://doi.org/10.1186/s40561-023-00237-x
Tracey, M. W. & Baaki, J. (2014). Design, designers, and reflection-in-action. In B. Hokanson &
A. Gibbons (Eds.), Design in educational technology: Design thinking, design process,
and the design studio (1st ed., pp. 1–13). Springer.
Tracey, M. W., & Baaki, J. (2022). Empathy and empathic design for meaningful deliverables.
Educational Technology Research and Development, 70(6), 2091-2116.
https://doi.org/10.1007/s11423-022-10146-4
Tracey, M. W., & Baaki, J. (2023). Tapping into how we teach what we teach: A journey in
explicit and implicit reflection. In B. Hokanson, M. Schmidt, M. Exter, A. Tawfik, & Y.
Earnshaw (Eds.), Formative design in learning: Design thinking, growth mindset and community (pp.241-50). Springer.
Tracey, M. W., Hutchinson, A., & Grzebyk, T. Q. (2014). Instructional designers as reflective
practitioners: Developing professional identity through reflection. Educational
Technology Research and Development, 62(3), 315-334.
https://doi.org/10.1007/s11423-014-9334-9
Trust, T., Whalen, J., & Mouza, C. (2023). Editorial: ChatGPT: Challenges, opportunities, and
implications for teacher education. Contemporary Issues in Technology and Teacher
Education, 23(1). https://citejournal.org/volume-23/issue-1-23/editorial/editorial-chatgpt-challenges-opportunities-and-implications-for-teacher-education
UNESCO. (2022). Recommendation on the ethics of artificial intelligence. United Nations
Educational, Scientific, and Cultural Organization (UNESCO).
https://unesdoc.unesco.org/ark:/48223/pf0000381137
Visscher-Voerman, I., Gustafson, K., & Plomp, T. (1999). Educational design and development:
An overview of paradigms. In van den Akker, J., Branch, R.M., Gustafson, K., Nieveen,
M., & Plomp, T. (Eds.), Design approaches and tools in education and training (pp. 15-
28). Springer.
Yang, M., & Watson, S. L. (2022). Attitudinal influences on transfer of training: A systematic
literature review. Performance Improvement Quarterly, 34(4), 327-365.
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