The rapid integration of artificial intelligence (AI) into educational research and practice has highlighted the need for clear and consistent reporting standards. The RAISE (Reporting AI Studies in Education) framework offers a structured checklist of 30 items across ten thematic domains, designed to guide authors in transparently documenting AI interventions, study design, learner context, data collection, outcomes, and findings. This editorial introduces RAISE, explains its rationale, and provides practical guidance for its application, including illustrative “Bad” and “Good” reporting examples. By promoting comprehensive and replicable reporting, RAISE aims to enhance the interpretability, reproducibility, and scholarly impact of AI-focused educational research, while supporting authors in meeting emerging expectations for transparency in the field.
AllisonJ.HwangG.-J.MayerR. E.PellasN.KarnalimO.de FreitasS.NgO.-L.HuangY.-M.HooshyarD.SeidmanR. H.Al-EmranM.MikropoulosT. A.SchroederN. L.RoscoeR. D.SanusiI. (2025). From generative AI to extended reality: Multidisciplinary perspectives on the challenges, opportunities, and future of educational computing. Journal of Educational Computing Research, 63(6), 1327–1363. https://doi.org/10.1177/07356331251359964
2.
CingilliogluI.GalU.ProkhorovA. (2024). AI-experiments in education: An AI-driven randomized controlled trial for higher education research. Education and Information Technologies, 29(15), 19649–19677. https://doi.org/10.1007/s10639-024-12633-y
3.
DaiY.LinZ.LiuA.DaiD.WangW. (2023). Effect of an analogy-based approach of artificial intelligence pedagogy in upper primary schools. Journal of Educational Computing Research, 61(8), 1695–1722. https://doi.org/10.1177/07356331231201342
4.
DengR.JiangM.YuX.LuY.LiuS. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
5.
DeschênesM. (2020). Recommender systems to support learners’ agency in a learning context: A systematic review. International Journal of Educational Technology in Higher Education, 17(1), 50. https://doi.org/10.1186/s41239-020-00219-w
6.
FederiakinD.MolerovD.Zlatkin-TroitschanskaiaO.MaurA. (2024). Prompt engineering as a new 21st-century skill. Frontiers in Education, 9, 1366434. https://doi.org/10.3389/feduc.2024.1366434
7.
FuY.WengZ. (2024). Navigating the ethical terrain of AI in education: A systematic review on framing responsible human-centered AI practices. Computers and Education: Artificial Intelligence, 7, 100306. https://doi.org/10.1016/j.caeai.2024.100306
8.
FunaA. A.GabayR. A. E. (2025). Policy guidelines and recommendations on AI use in teaching and learning: A meta-synthesis study. Social Sciences & Humanities Open, 11, 101221. https://doi.org/10.1016/j.ssaho.2024.101221
9.
García-LópezI. M.Trujillo-LiñánL. (2025). Ethical and regulatory challenges of generative AI in education: A systematic review. Frontiers in Education, 10, 1565938. https://doi.org/10.3389/feduc.2025.1565938
10.
GebruT.MorgensternJ.VecchioneB.VaughanJ. W.WallachH.DauméH.IIICrawfordK. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/345872
11.
GousetiA.JamesF.FallinL.BurdenK. (2025). The ethics of using AI in K-12 education: A systematic literature review. Technology, Pedagogy and Education, 34(2), 161–182. https://doi.org/10.1080/1475939X.2024.2428601
12.
GuizaniS.MazharT.ShahzadT.AhmadW.BibiA.HamamH. (2025). A systematic literature review to implement large language models in higher education: Issues and solutions. Discover Education, 4(1), 1–25. https://doi.org/10.1007/s44217-025-00424-7
13.
GünerH.ErE. (2025). AI in the classroom: Exploring students’ interaction with ChatGPT in programming learning. Education and Information Technologies, 30, 12681–12707. https://doi.org/10.1007/s10639-025-13337-7
14.
HuW.TianJ.LiY. (2025). Enhancing student engagement in online collaborative writing through a generative AI-based conversational agent. The Internet and Higher Education, 65, 100979. https://doi.org/10.1016/j.iheduc.2024.100979
15.
JacobsenL. J.WeberK. E. (2025). The promises and pitfalls of large language models as feedback providers: A study of prompt engineering and the quality of AI-driven feedback. AI, 6(2), 35. https://doi.org/10.3390/ai6020035
16.
KnothN.TolzinA.JansonA.LeimeisterJ. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225. https://doi.org/10.1016/j.caeai.2024.100225
17.
LeeD.PalmerE. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(1), 7. https://doi.org/10.1186/s41239-025-00503-7
18.
Liz-DomínguezM.Caeiro-RodríguezM.Llamas-NistalM.Mikic-FonteF. A. (2019). Systematic literature review of predictive analysis tools in higher education. Applied Sciences, 9(24), 5569. https://doi.org/10.3390/app9245569
19.
PageM. J.McKenzieJ. E.BossuytP. M.BoutronI.HoffmannT. C.MulrowC. D.ShamseerL.TetzlaffJ. M.AklE. A.BrennanS. E.ChouR.GlanvilleJ.GrimshawJ. M.HróbjartssonA.LaluM. M.LiT.LoderE. W.Mayo-WilsonE.McDonaldS.MoherD. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
20.
QianY. (2025). Prompt engineering in education: A systematic review of approaches and educational applications. Journal of Educational Computing Research, 0(0), 07356331251365189. https://doi.org/10.1177/07356331251365189
21.
RismanchianS.DoroudiS. (2025). The evolution of research on AI and education across four decades: Insights from the AIxEd framework. International Journal of Artificial Intelligence in Education, 1–24. https://doi.org/10.1007/s40593-025-00483-9
SongY.HuangL.ZhengL.FanM.LiuZ. (2025). Interactions with generative AI chatbots: Unveiling dialogic dynamics, students’ perceptions, and practical competencies in creative problem-solving. International Journal of Educational Technology in Higher Education, 22(1), 12. https://doi.org/10.1186/s41239-025-00508-2
24.
StefaniakJ. E.MooreS. L. (2024). The use of generative AI to support inclusivity and design deliberation for online instruction. Online Learning, 28(3), 181–205. https://doi.org/10.24059/olj.v28i3.4458
25.
TopaliP.HaelermansC.MolenaarI.SegersE. (2025). Pedagogical considerations in the automation era: A systematic literature review of AIEd in K-12 authentic settings. British Educational Research Journal, berj.4200–33. https://doi.org/10.1002/berj.4200
26.
WangX.HuangR.SommerM.PeiB.ShidfarP.RehmanM. S.RitzhauptA. D.MartinF. (2024). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Journal of Educational Computing Research, 62(6), 1348–1383. https://doi.org/10.1177/07356331241240459
27.
WuT.-T.LeeH.-Y.LiP.-H.HuangC.-N.HuangY.-M. (2023). Promoting self-regulation progress and knowledge construction in blended learning via ChatGPT-based learning aid. Journal of Educational Computing Research, 61(8), 1539–1567. https://doi.org/10.1177/07356331231191125
28.
ZhaoX.CoxA.ChenX. (2025). The use of generative AI by students with disabilities in higher education. The Internet and Higher Education, 66, Article 101014. https://doi.org/10.1016/j.iheduc.2025.101014
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