This study explores the capability of artificial intelligence tools to generate questions aligned with Bloom's Revised Taxonomy to support educators. On October 6, 2023, ChatGPT 4, Google Bard (now Gemini), and an experienced elementary educator were prompted to create questions for each Bloom's cognitive levels in math, science, and a popular book. Fifteen gifted education experts evaluated the AI- and human-generated questions for alignment with Bloom's Taxonomy. Findings show that AI-generated questions align well with lower-order cognitive tasks and remain consistent across subjects. AI demonstrates potential in higher-order tasks particularly in science and language arts, though human-generated questions provide greater alignment. Raters struggled to distinguish AI- from human-generated questions at lower levels but more easily identified human-generated questions in higher-order tasks. These results suggest that AI can help educators reduce workload and enhance differentiated instruction by generating questions aligned with Bloom's Taxonomy; however, professional learning is essential for supporting educators.
Al FarabyS.AdiwijayaA.RomadhonyA. (2023). Review on neural question generation for education purposes. International Journal of Artificial Intelligence in Education, 34, 1008–1045. https://doi.org/10.1007/s40593-023-00374-x
2.
AndersonL. W.KrathwohlD. R.BloomB. S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
3.
BloomB. S. (1969). Taxonomy of educational objectives: The classification of educational goals: Handbook I, Cognitive domain. McKay.
4.
CelikI.DindarM.MuukkonenH.JärveläS. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y
5.
ChenY.-C.HandB.Norton-MeierL. (2017). Teacher roles of questioning in early elementary science classrooms: A framework promoting student cognitive complexities in argumentation. Research in Science Education, 47, 373–405. https://doi.org/10.1007/S11165-015-9506-6
6.
CookC. (2000). A review of intraclass correlation [Conference presentation]. Southwest Educational Research Association Annual Meeting, Dallas, TX, United States. https://files.eric.ed.gov/fulltext/ED435705.pdf
7.
CreswellJ. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
8.
CukurovaM.LuckinR. (2018). Measuring the impact of emerging technologies in education: A pragmatic approach. In VoogtJ.KnezekG.ChristensenR.LaiK. W. (Eds.), Second handbook of information technology in primary and secondary education (pp. 1181–1199). Springer. https://www.springer.com/gp/book/9783319710532
9.
DanielsS. (1997). Creativity in the classroom: Characteristics, climate, and curriculum. In ColangeloN.DavisG. (Eds.), Handbook of gifted education (pp. 300–301). Allyn and Bacon.
10.
DasB.MajumderM.PhadikarS.SekhA. A. (2021). Automatic question generation and answer assessment: A survey. Research and Practice in Technology Enhanced Learning, 16(1), 5. https://doi.org/10.1186/s41039-021-00151-1
11.
DixonF. A.YsselN.McConnellJ. M.HardinT. (2014). Differentiated instruction, professional development, and teacher efficacy. Journal for the Education of the Gifted, 37(2), 111–127. https://doi.org/10.1177/0162353214529042
12.
FarkasS.DuffettA. (2008). High-achieving students in the era of NCLB. Fordham Institute.
13.
FeldhusenJ. F. (1994). Talent Identification and Development in Education (TIDE). Gifted Education International, 10(1), 10–15. https://doi.org/10.1177/026142949401000103
14.
GallagherJ. (1985). Teaching the gifted child. Allyn and Bacon.
15.
GibbsK. (2023). Voices in practice: Challenges to implementing differentiated instruction by teachers and school leaders in an Australian mainstream secondary school. The Australian Educational Researcher, 50(4), 1217–1232. https://doi.org/10.1007/s13384-022-00551-2
16.
GinjaT. G.ChenX. (2020). Teacher educators’ perspectives and experiences towards differentiated instruction. International Journal of Instruction, 13(4), 781–798. https://doi.org/10.29333/iji.2020.13448a
17.
HaydenS. M.GubbinsE. J.CodyR. A.BoldtG. T. (2024). Teachers’ perceptions of differentiation following a math curriculum implementation study. Journal for the Education of the Gifted, 47(1), 3–29. https://doi.org/10.1177/01623532231215092
18.
HolmesW.TuomiI. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
KaplanS. N. (1974). Providing programs for the gifted and talented: A handbook. Office of the Ventura County Superintendent of Schools.
21.
KilbaneC. R.MilmanN. B. (2014). Teaching models: Designing instruction for 21st century learners. Pearson.
22.
KrsmanovićG.DeekF. (2023, September). AI applications in education for working with gifted children: Future uses and psychosocial effects. Paper presented at the Mensa Sixth International Scientific and Professional Conference: “Working with the Gifted: Methods and Programs”, Novi Sad, Serbia.
23.
KurdiG.LeoJ.ParsiaB.SattlerU.Al-EmariS. (2020). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1), 121–204. https://doi.org/10.1007/s40593-019-00186-y
24.
LeeU.JungH.JeonY.SohnY.HwangW.MoonJ.KimH. (2023). Few-shot is enough: Exploring ChatGPT prompt engineering method for automatic question generation in English education. Education and Information Technologies, 29, 11483–11515. https://doi.org/10.1007/s10639-023-12249-8
25.
LoC. O.FengL.-C. (2020). Teaching higher order thinking skills to gifted students: A meta-analysis. Gifted Education International, 36(2), 196–217. https://doi.org/10.1177/0261429420917854
26.
MakerC. J. (1982). Curriculum development for the gifted. Aspen Systems.
27.
MarkowitzD. M.HancockJ. T.BailensonJ. N. (2023). Linguistic markers of inherently false AI communication and intentionally false human communication: Evidence from hotel reviews. Journal of Language and Social Psychology, 43(1), 63–82. https://doi.org/10.1177/0261927X231200201 (Original work published 2024).
28.
MehtaS.SunY.ZhuR.MurphyD. K.HardasB.KochG. (2018). Performance of intraclass correlation coefficient (ICC) as a reliability index under various distributions in scale reliability studies. Statistics in Medicine, 37(18), 2734–2752. https://doi.org/10.1002/sim.7679
29.
MullaN.GharpureP. (2023). Automatic question generation: A review of methodologies, datasets, evaluation metrics, and applications. Progress in Artificial Intelligence, 12(1), 1–32. https://doi.org/10.1007/s13748-023-00295-9
30.
PassowA. H. (1982). Differentiated curricula for the gifted/talented: A point of view. National/State Leadership Training Institute on the Gifted and Talented.
31.
RobertsJ. L.InmanT. F. (2022). Effective differentiation for continuous progress. In RobertsJ. L.InmanT. F.RobinsJ. H. (Eds.), Introduction to gifted education (2nd ed., pp. 243–265). Routledge.
RollI.WylieR. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
34.
RubensteinL. D.GilsonC. M.Bruce-DavisM. N.GubbinsE. J. (2015). Teachers’ reactions to pre-differentiated and enriched mathematics curricula. Journal for the Education of the Gifted, 38(2), 141–168. https://doi.org/10.1177/0162353215578280
35.
SalmonA. K.BarreraM. X. (2021). Intentional questioning to promote thinking and learning. Thinking Skills and Creativity, 40, https://doi.org/10.1016/j.tsc.2021.100822
36.
SantangeloT.TomlinsonC. A. (2012). Teacher educators’ perceptions and use of differentiated instruction practices: An exploratory investigation. Action in Teacher Education, 34(4), 309–327. https://doi.org/10.1080/01626620.2012.717032
37.
ScariaN.ChennaS. D.SubramaniD. (2024). Automated educational question generation at different Bloom’s skill levels using large language models: Strategies and evaluation. ArXiv.
38.
SchellK. L.OswaldF. L. (2013). Item grouping and item randomization in personality measurement. Personality and Individual Differences, 55(3), 317–321. https://doi.org/10.1016/j.paid.2013.03.008
39.
SchwartzD. L.TsangJ. M.BlairK. P. (2016). The ABCs of how we learn: 26 scientifically proven approaches, how they work, and when to use them (pp. 262–275). W.W. Norton & Company.
40.
ScottM. T. (2014). Using the Bloom’s–Banks Matrix to develop multicultural differentiated lessons for gifted students. Gifted Child Today, 37(3), 163–168. https://doi.org/10.1177/1076217514532275
ShenZ.ZhangL.XiaoX.LiR.LiangR. (2020). Icon familiarity affects the performance of complex cognitive tasks. I-Perception, 11(2). https://doi.org/10.1177/2041669520910167
43.
ShimmeiM.BierN.MatsudaN. (2023). Machine-generated questions attract instructors when acquainted with learning objectives. In WangN.Rebolledo-MendezG.SantosO. C.DimitrovaV.MatsudaN. (Eds.), Artificial intelligence in education: 24th international conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, proceedings (pp. 3–15). Springer.
SiegleD. (2024). Using artificial intelligence (AI) technology to support the three legs of talent development. Gifted Child Today, 47(3), 221–227. https://doi.org/10.1177/10762175241242495
47.
TomlinsonC. (2017). The differentiated classroom: Responding to the needs of all learners. ASCD.
48.
United Nations. (2015). Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. https://sdgs.un.org/goals/goal4
VanTassel-BaskaJ. (2014). Curriculum issues: Artful inquiry: The use of questions in working with the gifted. Gifted Child Today, 37(1), 48–50. https://doi.org/10.1177/1076217513509621
51.
VanTassel-BaskaJ.BrownE. F. (2015). An analysis of gifted education curriculum models. In KarnesF. A.BeanS. M. (Eds.), Methods and materials for teaching the gifted (4th ed., pp. 107–138). Routledge.
WangJ.WangY.MooreY.SneedS.ThackerB.HartS. (2023). Analyzing the patterns of questioning chains and their intervention on student learning in science teacher preparation. International Journal of Science and Mathematics Education, 22, 809–836. https://doi.org/10.1007/s10763-023-10408-4
54.
WonY. (2019). The effect of task complexity on rater severity in an adaptive performance-based second language oral communication test [Unpublished doctoral dissertation]. Iowa State University.
55.
XuW.OuyangF. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3), 4195–4223. https://doi.org/10.1007/s10639-021-10774-y
56.
YağciA.ÇevikM. (2019). Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia). Education and Information Technologies, 24(5), 2741–2761. https://doi.org/10.1007/s10639-019-09885-4
