Restricted accessReview articleFirst published online 2026-3
Responsible Innovation in Designing AI for Education: Attending to the “How,” the “for What,” the “for Whom,” and the “With Whom” in a Rapidly Growing Field
Artificial intelligence (AI) in education is growing rapidly. Research has long played a role in developing knowledge of AI’s capabilities. Now is a critical time for investigating new approaches for design that center discussion of the purposes for AI in schools and that give youth, educators, and communities a say in those purposes. We argue for responsible and equitable design and development. We emphasize considering for whom AI is designed, involving diverse interest holders in its creation, and aligning AI with ethical principles and educational goals that support democratic and collaborative learning so that AI might support a more expansive range of purposes for learning than have been pursued in the past.
BenjaminR. (2019). Race after technology: Abolitionist tools for the New Jim Code. Wiley.
2.
BreidebandT.ChangM. A.BushJ. B.ReitmanJ. G.PenuelW. R.D’MelloS. K. (in press). CoBi: AI-based collaboration support and community building for K-12 classrooms. International Journal of Artificial Intelligence in Education.
3.
BushJ. B.BiddyQ.ChangM. A.Benedis-GrabG.BreidebandT.SumnerT.PenuelW. R.WeatherleyJ.SinghT.D’MelloS. K. (2025). Curriculum routines and a community builder AI agent: Synergy to mediate the convergent tendencies of AI. In OshimaJ.ChenB.VogelF.JärveläS. (Eds.), Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning (CSCL) (pp. 614–616). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2025.113295
4.
ChandlerC.BreidebandT.ReitmanJ. G.ChitwoodM.BushJ. B.HowardA.LeonhartS.FoltzP. W.PenuelW. R.D’MelloS. (2024). Computational modeling of collaborative discourse to enable feedback and reflection in middle school classrooms. In LAK '24: Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 576–586). Association for Computing Machinery. https://doi.org/10.1145/3636555.3636917
5.
ChangM. A.PhilipT. M.CortezA.McKoyA.SumnerT.PenuelW. R. (2022). Engaging youth in envisioning artificial intelligence in classrooms: Lessons learned. Digital Promise and the International Society of the Learning Sciences. https://repository.isls.org//handle/1/7670
6.
ChangM. A.WongR.BreidebandT.PhilipT. M.McKoyA.CortezA.D’MelloS. K. (2024). Co-design partners as transformative learners: Imagining ideal technology for schools by centering speculative relationships. In MuellerF.KyburzP.WilliamsonJ.SasC.ShklovskiI.DugasP. T.WilsonM. L. (Eds.), CHI ’24: Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642559
7.
ChineD. R.BrentleyC.Thomas-BrowneC.RicheyJ. E.GulA.CarvalhoP. F.BranstetterL.KoedingerK. R. (2022). Educational equity through combined human-AI personalization: A propensity matching evaluation. In RodrigoM. M.MatsudaN.CristeaA. I.DimitrovaV. (Eds.), Artificial Intelligence in Education. AIED 2022 (pp. 366–377). Springer. https://doi.org/10.1007/978-3-031-11644-5_30
8.
D’MelloS. K.BiddyQ.BreidebandT.BushJ. B.ChangM. A.CortezA.FlaniganJ.FoltzP. W.GormanJ. C.HirshfieldL. M.KoM.-L. M.KrishnaswamyN.LieberR.MartinJ.PalmerM. S.PenuelW. R.WalkerL.WhitehillJ. (2024). From learning optimization to learner flourishing: Reimagining AI in education at the Institute for Student-AI Teaming (iSAT). AI Magazine. https://doi.org/10.1002/aaai.12158
9.
D’MelloS. K.GraesserA. (2023). Intelligent tutoring systems: How computers achieve learning gains that rival human tutors. In SchutzP. A.MuisK. R. (Eds.), Handbook of educational psychology (4th ed., pp. 603–629). Routledge.
10.
DeBargerA. H.ChoppinJ. M.BeauvineauY.MoorthyS. (2013). Designing for productive adaptations of curriculum interventions. National Society for the Study of Education Yearbook, 112(2), 298–319.
11.
DeBargerA. H.PenuelW. R.HarrisC. J.SchankP. (2010). Teaching routines to enhance collaboration using classroom network technology. In PozziF.PersicoD. (Eds.), Techniques for fostering collaboration in online learning communities: Theoretical and practical perspectives (pp. 222–244). IGI Global.
12.
DeweyJ. (1916). Democracy and education. Macmillan.
13.
DillenbourgP.PrietoL. P.OlsenJ. K. (2018). Classroom orchestration. In FischerF.Hmelo-SilverC.GoldmanS. R.ReimannP. (Eds.), International handbook of the learning sciences (pp. 180–190). Routledge.
14.
DumontH.ReadyD. D. (2023). On the promise of personalized learning for educational equity. npj Science of Learning, 8, Article 26. https://doi.org/10.1038/s41539-023-00174-x
15.
FishmanB. J.PenuelW. R. (2018). Design-based implementation research. In FischerF.Hmelo-SilverC. E.GoldmanS. R.ReimannP. (Eds.), International handbook of the learning sciences (pp. 393–400). Routledge.
GrandbastienM.LuckinR.MizoguchiR.AlevenV. (2016). Prevace to the IJAIED 25th Anniversary Issue. International Journal of Artificial Intelligence in Education, 26, 1–3. https://doi.org/10.1007/s40593-015-0092-6
18.
HolsteinK.McLarenB. M.AlevenV. (2017). Intelligent tutors as teachers’ aides: Exploring teacher needs for real-time analytics in blended classrooms. In WiseA. F.WinneP. H.LynchG.OchoaX.MolenaarI.DawsonS.HatalaM. (Eds.), LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 257–266). Association for Computing Machinery. https://doi.org/10.1145/3027385.3027451
19.
KochmarE.VuD. D.GuptaV.SerbanI. V.PineauJ. (2022). Automated data-driven generation of personalized pedagogical interventions in Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education, 32, 323–349. https://doi.org/10.1007/s40593-021-00267-x
20.
KoedingerK.CorbettA. T.PerfettiC. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice divide to enhance robust student learning. Cognitive Science, 36(5), 757–798. https://doi.org/10.1111/j.1551-6709.2012.01245.x
21.
LizárragaJ. R. (2023). Cyborg sociopolitical reconfigurations: Designing for speculative fabulation in learning. Journal of the Learning Sciences, 32(1), 21–44. https://doi.org/10.1080/10508406.2022.2154159
22.
ManzE.SuárezE. (2018). Supporting teachers to negotiate uncertainty for science, students, and teaching. Science Education, 102(4), 771–795. https://doi.org/10.1002/sce.21343
National Academies of Sciences Engineering and Medicine. (2025). Equity in preK-12 STEM education: Framing decisions for the future. National Academies Press. https://doi.org/10.17226/26859
25.
National Research Council. (2001). Adding it up: Helping children learn mathematics. National Academies Press.
26.
NyeB. D.GraesserA. C.HuX. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. https://doi.org/10.1007/s40593-014-0029-5
27.
Organisation for Economic Co-operation and Development [OECD]. (2015). PISA 2015 collaborative problem solving framework. OECD. https://doi.org/10.1787/9789264281820-en
28.
OstranderA.BonnerD.WaltonJ.SlavinaA.OuversonK.KohlA.GilbertS.DorneichM.SinatraA.WinerE. (2020). Evaluation of an intelligent team tutoring system for a collaborative two-person problem: Surveillance. Computers in Human Behavior, 104, Article 105873. https://doi.org/10.1016/j.chb.2019.01.006
29.
PeurachD. J.PenuelW. R.RussellJ. L. (2019). Beyond ritualized rationality: Organizational dynamics of instructionally-focused continuous improvement. In ConnollyM.SpicerD. H. EddyJamesC.KruseS. D. (Eds.), SAGE international handbook of school organization (pp. 465–488). SAGE.
30.
PhilipT. M.BangM.JacksonK. (2018). Articulating the “how,” the “for what,” the “for whom,” and the “with whom” in concert: A call to broaden the benchmarks of our scholarship. Cognition and Instruction, 36(2), 83–88. https://doi.org/10.1080/07370008.2018.1413530
31.
PhilipT. M.SenguptaP. (2021). Theories of learning as theories of society: A contrapuntal approach to expanding disciplinary authenticity in computing. Journal of the Learning Sciences, 30(2), 330–349. https://doi.org/10.1080/10508406.2020.1828089
32.
ReckhowS.Tomkins-StangeM.Galey-HornS. (2021). How the political economy of knowledge production shapes education policy: The case of teacher evaluation in federal policy discourse. Educational Evaluation and Policy Analysis, 43(3), 472–494. https://doi.org/10.3102/01623737211003906
33.
ReichJ.DukesJ. (2024). Toward a new theory of arrival technologies: The case of ChatGPT and the future of education technology after adoption. EdArXiv. https://doi.org/10.35542/osf.io/x6vn7
34.
ReiserB. J.NovakM.McGillT. A. W.PenuelW. R. (2021). Storyline units: An instructional model to support coherence from the students’ perspective. Journal of Science Teacher Education, 32(7), 805–829. https://doi.org/10.1080/1046560X.2021.1884784
35.
RitterS.AndersonJ.KoedingerK. R.CorbettA. T. (2007). Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249–255. https://doi.org/10.3758/BF03194060
36.
RitterS.KulikowichJ. M.LeiP.McGuireC.MorganP. (2007). What evidence matters? A randomized field trial of Cognitive Tutor Algebra I. In HirashimaT.HoppeH. U.YoungS. S.-C. (Eds.), Supporting learning flow through integrative technologies (pp. 13–20). IOS Press.
37.
RoséC.WangY.-C.CuiY.ArguelloJ.StegmannK.WeinbergerrA.FischerF. (2008). Analyzing collaborative learning processes automatically: Exploiting the advcances of computational linguistics in computer-supported collaborative learning. International Journal of Computer Supported Cooperative Learning, 3(3), 237–237.
38.
ScardamaliaM.BereiterC.LamonM. (1994). The CSILE Project: Trying to bring the classroom into World 3. In McGillyK. (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 201–228). MIT Press.
SunC.ShuteV. J.StewartA.YonehiroJ.DuranN.D’MelloS. K. (2020). Towards a generalized competency model of collaborative problem solving. Computers and Education, 143, Article 103672. https://doi.org/10.1016/j.compedu.2019.103672
41.
VanLehnK. (2016). Regulative loops, step loops and task loops. International Journal of Artificial Intelligence in Education, 26(1), 107–112. https://doi.org/10.1007/s40593-015-0056-x
42.
WarrenB.VossoughiS.RoseberyA.BangM.TaylorE. (2020). Multiple ways of knowing: Re-imagining disciplinary learning. In NasirN. S.LeeC. D.PeaR.de RoystonM. McKinney (Eds.), Handbook of the cultural foundations of learning (pp. 277–293). Routledge.
43.
WiseA. F.KnightS.ShumS. B. (2021). Collaborative learning analytics. In CressU.RoséC. P.WiseA. F.OshimaJ. (Eds.), International handbook of computer-supported collaborative learning (pp. 425–443). Springer. https://doi.org/10.1007/978-3-030-65291-3_23
44.
YannierN.HudsonS. E.KoedingerK. (2020). Active learning is about more than hands-on: A mixed-reality AI system to support STEM education. International Journal of Artificial Intelligence in Education, 30, 74–76. https://doi.org/10.1007/s40593-020-00194-3
