{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T05:59:40Z","timestamp":1774763980956,"version":"3.50.1"},"publisher-location":"Cham","reference-count":93,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031440663","type":"print"},{"value":"9783031440670","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44067-0_3","type":"book-chapter","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T06:02:33Z","timestamp":1697781753000},"page":"48-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Explainable Artificial Intelligence in Education: A Comprehensive Review"],"prefix":"10.1007","author":[{"given":"Blerta Abazi","family":"Chaushi","sequence":"first","affiliation":[]},{"given":"Besnik","family":"Selimi","sequence":"additional","affiliation":[]},{"given":"Agron","family":"Chaushi","sequence":"additional","affiliation":[]},{"given":"Marika","family":"Apostolova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"3_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-52240-7_1","volume-title":"AIED 2020","author":"S Abdi","year":"2020","unstructured":"Abdi, S., Khosravi, H., Sadiq, S.: Modelling learners in crowdsourcing educational systems. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Mill\u00e1n, E. (eds.) AIED 2020. LNCS, vol. 12164, pp. 3\u20139. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52240-7_1"},{"issue":"9","key":"3_CR2","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1080\/03075079.2011.616584","volume":"38","author":"A Aditomo","year":"2013","unstructured":"Aditomo, A., Goodyear, P., Bliuc, A.-M., Ellis, R.A.: Inquiry-based learning in higher education: principal forms, educational objectives, and disciplinary variations. Stud. High. Educ. 38(9), 1239\u20131258 (2013)","journal-title":"Stud. High. Educ."},{"key":"3_CR3","unstructured":"Aditya, B.: Applied Machine Learning Explainability Techniques: Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More. Packt Publishing Ltd. (2022)"},{"key":"3_CR4","first-page":"100052","volume":"3","author":"IA Akour","year":"2022","unstructured":"Akour, I.A., Al-Maroof, R.S., Alfaisal, R., Salloum, S.A.: A conceptual framework for determining metaverse adoption in higher institutions of gulf area: an empirical study using hybrid SEM-ANN approach. Comput. Educ.: Artif. Intell. 3, 100052 (2022)","journal-title":"Comput. Educ.: Artif. Intell."},{"issue":"12","key":"3_CR5","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.14778\/3554821.3554900","volume":"15","author":"S Amer-Yahia","year":"2022","unstructured":"Amer-Yahia, S.: Towards AI-powered data-driven education. Proc. VLDB Endow. 15(12), 3798\u20133806 (2022)","journal-title":"Proc. VLDB Endow."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Amorim, E., Can\u00e7ado, M., Veloso, A.: Automated essay scoring in the presence of biased ratings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 229\u2013237 (2018)","DOI":"10.18653\/v1\/N18-1021"},{"key":"3_CR7","unstructured":"Artificial intelligence\u2014OECD. (n.d.). https:\/\/www.oecd.org\/digital\/artificial-intelligence\/.Accessed 26 Apr 2023"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Baker, R., Inventado, P.: Educational data mining and learning analytics, pp. 61\u201375 (2014). https:\/\/doi.org\/10.1007\/978-1-4614-3305-7_4","DOI":"10.1007\/978-1-4614-3305-7_4"},{"issue":"1","key":"3_CR9","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s13347-022-00510-w","volume":"35","author":"K Baum","year":"2022","unstructured":"Baum, K., Mantel, S., Schmidt, E., Speith, T.: From responsibility to reason-giving explainable artificial intelligence. Philos. Technol. 35(1), 12 (2022). https:\/\/doi.org\/10.1007\/s13347-022-00510-w","journal-title":"Philos. Technol."},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"100068","DOI":"10.1016\/j.caeai.2022.100068","volume":"3","author":"A Bhutoria","year":"2022","unstructured":"Bhutoria, A.: Personalized education and artificial intelligence in the united states, china, and India: a systematic review using a human-in-the-loop model. Comput. Educ.: Artif. Intell. 3, 100068 (2022). https:\/\/doi.org\/10.1016\/j.caeai.2022.100068","journal-title":"Comput. Educ.: Artif. Intell."},{"key":"3_CR11","unstructured":"Bischl, B., et al.: Openml benchmarking suites. ArXiv Preprint ArXiv:1708.03731 (2017)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Blikstein, P.: Gears of our childhood: constructionist toolkits, robotics, and physical computing, past and future. In: Proceedings of the 12th International Conference on Interaction Design and Children, pp. 173\u2013182 (2013)","DOI":"10.1145\/2485760.2485786"},{"key":"3_CR13","unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. ArXiv Preprint ArXiv:1604.07316 (2016)"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Bostrom, N., Yudkowsky, E.: The Ethics of artificial intelligence, pp. 57\u201369 (2018). https:\/\/doi.org\/10.1201\/9781351251389-4","DOI":"10.1201\/9781351251389-4"},{"key":"3_CR15","unstructured":"Boyd-Graber, J., Satinoff, B., He, H., Daum\u00e9, I.: Besting the quiz master: crowdsourcing incremental classification games, p. 1301 (2012)"},{"issue":"3","key":"3_CR16","first-page":"337","volume":"43","author":"P Brusilovsky","year":"2022","unstructured":"Brusilovsky, P., Sosnovsky, S., Thaker, K.: The return of intelligent textbooks. AI Mag. 43(3), 337\u2013340 (2022)","journal-title":"AI Mag."},{"key":"3_CR17","unstructured":"Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77\u201391 (2018)"},{"issue":"1","key":"3_CR18","first-page":"173","volume":"21","author":"T Cavanagh","year":"2020","unstructured":"Cavanagh, T., Chen, B., Lahcen, R.A.M., Paradiso, J.R.: Constructing a design framework and pedagogical approach for adaptive learning in higher education: a practitioner\u2019s perspective. Int. Rev. Res. Open Distrib. Learn. 21(1), 173\u2013197 (2020)","journal-title":"Int. Rev. Res. Open Distrib. Learn."},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Chen, Z.: Artificial intelligence-virtual trainer: innovative didactics aimed at personalized training needs. J. Knowl. Econ. 1\u201319 (2022)","DOI":"10.1007\/s13132-022-00985-0"},{"issue":"1","key":"3_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.iheduc.2011.06.002","volume":"15","author":"N Dabbagh","year":"2012","unstructured":"Dabbagh, N., Kitsantas, A.: Personal learning environments, social media, and self-regulated learning: a natural formula for connecting formal and informal learning. Internet High. Educ. 15(1), 3\u20138 (2012). https:\/\/doi.org\/10.1016\/j.iheduc.2011.06.002","journal-title":"Internet High. Educ."},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Dillenbourg, P., Jermann, P.: Technology for classroom orchestration. New Sci. Learn.: Cogn. Comput. Collab. Educ. 525\u2013552 (2010)","DOI":"10.1007\/978-1-4419-5716-0_26"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv:1702.08608 (2017). https:\/\/doi.org\/10.48550\/arXiv.1702.08608","DOI":"10.48550\/arXiv.1702.08608"},{"issue":"6248","key":"3_CR23","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1126\/science.aaa9375","volume":"349","author":"C Dwork","year":"2015","unstructured":"Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., Roth, A.: The reusable holdout: preserving validity in adaptive data analysis. Science 349(6248), 636\u2013638 (2015)","journal-title":"Science"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Ehsan, U., et al.: Human-centered explainable AI (HCXAI): beyond opening the black-box of AI. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1\u20137 (2022)","DOI":"10.1145\/3491101.3503727"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Epstein, Z., Foppiani, N., Hilgard, S., Sharma, S., Glassman, E., Rand, D.: Do explanations increase the effectiveness of AI-crowd generated fake news warnings? In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16, pp. 183\u2013193 (2022)","DOI":"10.1609\/icwsm.v16i1.19283"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Green, E., Chia, R., Singh, D.: AI ethics and higher education\u2014good practice and guidance for educators, learners, and institutions. Globethics.net (2022)","DOI":"10.58863\/20.500.12424\/4146302"},{"issue":"7639","key":"3_CR27","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115\u2013118 (2017)","journal-title":"Nature"},{"key":"3_CR28","unstructured":"Farrow, R.: The possibilities and limits of XAI in education: a socio-technical perspective. Learn. Media Technol. 1\u201314 (2023)"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80\u201389 (2018)","DOI":"10.1109\/DSAA.2018.00018"},{"key":"3_CR30","doi-asserted-by":"publisher","first-page":"102544","DOI":"10.1016\/j.conb.2022.102544","volume":"73","author":"NL Goodwin","year":"2022","unstructured":"Goodwin, N.L., Nilsson, S.R., Choong, J.J., Golden, S.A.: Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience. Curr. Opin. Neurobiol. 73, 102544 (2022)","journal-title":"Curr. Opin. Neurobiol."},{"key":"3_CR31","unstructured":"Grand View Research. AI In Education Market Size & Share Report, 2022\u20132030, p. 100 (2021). https:\/\/www.grandviewresearch.com\/industry-analysis\/artificial-intelligence-ai-education-market-report"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.-Z.: XAI\u2014explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019)","DOI":"10.1126\/scirobotics.aay7120"},{"key":"3_CR33","unstructured":"Herman, B.: The promise and peril of human evaluation for model interpretability. ArXiv Preprint ArXiv:1711.07414 (2017)"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Holmes, W., Porayska-Pomsta, K.: The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. Taylor & Francis (2022)","DOI":"10.4324\/9780429329067"},{"key":"3_CR35","unstructured":"HolonIQ. Artificial Intelligence in Education. 2023 Survey Insights (2023). https:\/\/www.holoniq.com\/notes\/artificial-intelligence-in-education-2023-survey-insights"},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Holstein, K., Wortman Vaughan, J., Daum\u00e9 III, H., Dudik, M., Wallach, H.: Improving fairness in machine learning systems: what do industry practitioners need? In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1\u201316 (2019)","DOI":"10.1145\/3290605.3300830"},{"key":"3_CR37","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-99740-7_1","volume-title":"CD-MAKE 2018","author":"A Holzinger","year":"2018","unstructured":"Holzinger, A., Kieseberg, P., Weippl, E., Tjoa, A.M.: Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2018. LNCS, vol. 11015, pp. 1\u20138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99740-7_1"},{"key":"3_CR38","doi-asserted-by":"publisher","first-page":"100001","DOI":"10.1016\/j.caeai.2020.100001","volume":"1","author":"G-J Hwang","year":"2020","unstructured":"Hwang, G.-J., Xie, H., Wah, B.W., Ga\u0161evi\u0107, D.: Vision, challenges, roles and research issues of artificial intelligence in education. Comput. Educ.: Artif. Intell. 1, 100001 (2020). https:\/\/doi.org\/10.1016\/j.caeai.2020.100001","journal-title":"Comput. Educ.: Artif. Intell."},{"key":"3_CR39","unstructured":"IBM Research. Project Debater (n.d.). https:\/\/research.ibm.com\/interactive\/project-debater\/. Accessed 26 Apr 2023"},{"key":"3_CR40","doi-asserted-by":"publisher","unstructured":"Islam, M.R., Ahmed, M.U., Barua, S., Begum, S.: A Systematic review of explainable artificial intelligence in terms of different application domains and tasks. Appl. Sci. 12(3), Article 3 (2022). https:\/\/doi.org\/10.3390\/app12031353","DOI":"10.3390\/app12031353"},{"key":"3_CR41","doi-asserted-by":"publisher","first-page":"102274","DOI":"10.1016\/j.lindif.2023.102274","volume":"103","author":"E Kasneci","year":"2023","unstructured":"Kasneci, E., et al.: ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274 (2023)","journal-title":"Learn. Individ. Differ."},{"key":"3_CR42","doi-asserted-by":"publisher","unstructured":"Kelley, S., Ovchinnikov, A., Ramolete, G., Sureshbabu, K., Heinrich, A.: Tailoring explainable artificial intelligence: user preferences and profitability implications for firms. SSRN Scholarly Paper No. 4305480 (2022). https:\/\/doi.org\/10.2139\/ssrn.4305480","DOI":"10.2139\/ssrn.4305480"},{"key":"3_CR43","doi-asserted-by":"publisher","unstructured":"Khosravi, H., Gyamfi, G., Hanna, B.E., Lodge, J.: Fostering and supporting empirical research on evaluative judgement via a crowdsourced adaptive learning system. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 83\u201388 (2020). https:\/\/doi.org\/10.1145\/3375462.3375532","DOI":"10.1145\/3375462.3375532"},{"key":"3_CR44","doi-asserted-by":"publisher","first-page":"100074","DOI":"10.1016\/j.caeai.2022.100074","volume":"3","author":"H Khosravi","year":"2022","unstructured":"Khosravi, H., et al.: Explainable artificial intelligence in education. Comput. Educ.: Artif. Intell. 3, 100074 (2022). https:\/\/doi.org\/10.1016\/j.caeai.2022.100074","journal-title":"Comput. Educ.: Artif. Intell."},{"key":"3_CR45","doi-asserted-by":"publisher","first-page":"113302","DOI":"10.1016\/j.dss.2020.113302","volume":"134","author":"B Kim","year":"2020","unstructured":"Kim, B., Park, J., Suh, J.: Transparency and accountability in AI decision support: explaining and visualizing convolutional neural networks for text information. Decis. Support Syst. 134, 113302 (2020)","journal-title":"Decis. Support Syst."},{"issue":"5","key":"3_CR46","doi-asserted-by":"publisher","first-page":"6069","DOI":"10.1007\/s10639-021-10831-6","volume":"27","author":"J Kim","year":"2022","unstructured":"Kim, J., Lee, H., Cho, Y.H.: Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Educ. Inf. Technol. 27(5), 6069\u20136104 (2022). https:\/\/doi.org\/10.1007\/s10639-021-10831-6","journal-title":"Educ. Inf. Technol."},{"key":"3_CR47","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.compedu.2016.10.001","volume":"104","author":"RF Kizilcec","year":"2017","unstructured":"Kizilcec, R.F., P\u00e9rez-Sanagust\u00edn, M., Maldonado, J.J.: Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Comput. Educ. 104, 18\u201333 (2017)","journal-title":"Comput. Educ."},{"issue":"1","key":"3_CR48","first-page":"237","volume":"133","author":"J Kleinberg","year":"2018","unstructured":"Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., Mullainathan, S.: Human decisions and machine predictions. Q. J. Econ. 133(1), 237\u2013293 (2018)","journal-title":"Q. J. Econ."},{"issue":"3","key":"3_CR49","doi-asserted-by":"publisher","first-page":"249","DOI":"10.21692\/haps.2018.032","volume":"22","author":"V Kolchenko","year":"2018","unstructured":"Kolchenko, V.: Can modern AI replace teachers? Not so fast! Artificial intelligence and adaptive learning: personalized education in the AI age. HAPS Educator 22(3), 249\u2013252 (2018)","journal-title":"HAPS Educator"},{"key":"3_CR50","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-031-18292-1_4","volume-title":"Explainable Edge AI: A Futuristic Computing Perspective","author":"M Kumari","year":"2023","unstructured":"Kumari, M., Chaudhary, A., Narayan, Y.: Explainable AI (XAI): a survey of current and future opportunities. In: Hassanien, A.E., Gupta, D., Singh, A.K., Garg, A. (eds.) Explainable Edge AI: A Futuristic Computing Perspective. Studies in Computational Intelligence, vol. 1072, pp. 53\u201371. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-18292-1_4"},{"issue":"7","key":"3_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1108\/INTR-08-2021-0600","volume":"32","author":"S Laato","year":"2022","unstructured":"Laato, S., Tiainen, M., Najmul Islam, A.K.M., M\u00e4ntym\u00e4ki, M.: How to explain AI systems to end users: a systematic literature review and research agenda. Internet Res. 32(7), 1\u201331 (2022). https:\/\/doi.org\/10.1108\/INTR-08-2021-0600","journal-title":"Internet Res."},{"key":"3_CR52","doi-asserted-by":"publisher","first-page":"100126","DOI":"10.1016\/j.caeai.2023.100126","volume":"4","author":"M Laupichler","year":"2023","unstructured":"Laupichler, M., Aster, A., Tobias, R.: Delphi study for the development and preliminary validation of an item set for the assessment of non-experts\u2019 AI literacy. Comput. Educ.: Artif. Intell. 4, 100126 (2023). https:\/\/doi.org\/10.1016\/j.caeai.2023.100126","journal-title":"Comput. Educ.: Artif. Intell."},{"issue":"3","key":"3_CR53","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31\u201357 (2018)","journal-title":"Queue"},{"issue":"6","key":"3_CR54","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1093\/jcmc\/zmab013","volume":"26","author":"B Liu","year":"2021","unstructured":"Liu, B.: In AI we trust? Effects of agency locus and transparency on uncertainty reduction in human\u2013AI interaction. J. Comput.-Mediat. Commun. 26(6), 384\u2013402 (2021)","journal-title":"J. Comput.-Mediat. Commun."},{"issue":"3","key":"3_CR55","first-page":"202","volume":"14","author":"TR Liyanagunawardena","year":"2013","unstructured":"Liyanagunawardena, T.R., Adams, A.A., Williams, S.A.: MOOCs: a systematic study of the published literature 2008\u20132012. Int. Rev. Res. Open Distrib. Learn. 14(3), 202\u2013227 (2013)","journal-title":"Int. Rev. Res. Open Distrib. Learn."},{"key":"3_CR56","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"3_CR57","unstructured":"MacGillis, A.: The students left behind by remote learning. ProPublica (2020). https:\/\/www.propublica.org\/article\/the-students-left-behind-by-remote-learning"},{"key":"3_CR58","series-title":"CCIS","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1007\/978-3-031-22918-3_36","volume-title":"TECH-EDU 2022","author":"R Manhi\u00e7a","year":"2023","unstructured":"Manhi\u00e7a, R., Santos, A., Cravino, J.: The impact of artificial intelligence on a learning management system in a higher education context: a position paper. In: Reis, A., Barroso, J., Martins, P., Jimoyiannis, A., Huang, R.Y.M., Henriques, R. (eds.) TECH-EDU 2022. CCIS, vol. 1720, pp. 454\u2013460. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-22918-3_36"},{"key":"3_CR59","unstructured":"Meacham, M.: A brief history of AI and education. Int. J. Adult Non Formal Educ. 1\u20132 (2021)"},{"issue":"5","key":"3_CR60","doi-asserted-by":"publisher","first-page":"3503","DOI":"10.1007\/s10462-021-10088-y","volume":"55","author":"D Minh","year":"2022","unstructured":"Minh, D., Wang, H.X., Li, Y.F., Nguyen, T.N.: Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev. 55(5), 3503\u20133568 (2022). https:\/\/doi.org\/10.1007\/s10462-021-10088-y","journal-title":"Artif. Intell. Rev."},{"issue":"11","key":"3_CR61","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1038\/s42256-019-0114-4","volume":"1","author":"B Mittelstadt","year":"2019","unstructured":"Mittelstadt, B.: Principles alone cannot guarantee ethical AI. Nat. Mach. Intell. 1(11), 501\u2013507 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"3_CR62","first-page":"102888","volume":"54","author":"J Moon","year":"2022","unstructured":"Moon, J., Rho, S., Baik, S.W.: Toward explainable electrical load forecasting of buildings: a comparative study of tree-based ensemble methods with Shapley values. Sustain. Energy Technol. Assess 54, 102888 (2022)","journal-title":"Sustain. Energy Technol. Assess"},{"key":"3_CR63","doi-asserted-by":"publisher","unstructured":"Nagahisarchoghaei, M., et al.: An empirical survey on explainable ai technologies: recent trends, use-cases, and categories from technical and application perspectives. Electronics 12(5), Article 5 (2023). https:\/\/doi.org\/10.3390\/electronics12051092","DOI":"10.3390\/electronics12051092"},{"key":"3_CR64","volume-title":"Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods","author":"A Nandi","year":"2022","unstructured":"Nandi, A., Pal, A.K.: Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods. Springer, Heidelberg (2022)"},{"key":"3_CR65","unstructured":"Needham, Mass.: Worldwide spending on AI-centric systems forecast to reach $154 billion in 2023, according to IDC. IDC: The Premier Global Market Intelligence Company (2023). https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS50454123"},{"issue":"4","key":"3_CR66","doi-asserted-by":"publisher","first-page":"4221","DOI":"10.1007\/s10639-022-11316-w","volume":"28","author":"A Nguyen","year":"2023","unstructured":"Nguyen, A., Ngo, H.N., Hong, Y., Dang, B., Nguyen, B.-P.T.: Ethical principles for artificial intelligence in education. Educ. Inf. Technol. 28(4), 4221\u20134241 (2023). https:\/\/doi.org\/10.1007\/s10639-022-11316-w","journal-title":"Educ. Inf. Technol."},{"issue":"6","key":"3_CR67","doi-asserted-by":"publisher","first-page":"7893","DOI":"10.1007\/s10639-022-10925-9","volume":"27","author":"F Ouyang","year":"2022","unstructured":"Ouyang, F., Zheng, L., Jiao, P.: Artificial intelligence in online higher education: a systematic review of empirical research from 2011 to 2020. Educ. Inf. Technol. 27(6), 7893\u20137925 (2022)","journal-title":"Educ. Inf. Technol."},{"key":"3_CR68","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Scheuerman, M.K., Amironesei, R.: You can\u2019t sit with us: exclusionary pedagogy in AI ethics education. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 515\u2013525 (2021)","DOI":"10.1145\/3442188.3445914"},{"key":"3_CR69","doi-asserted-by":"crossref","unstructured":"Ratliff, K.: Building rapport and creating a sense of community: are relationships important in the online classroom? J. Online Learn. Res. Pract. 7(1) (2019)","DOI":"10.18278\/il.7.1.4"},{"key":"3_CR70","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"3_CR71","doi-asserted-by":"publisher","first-page":"110273","DOI":"10.1016\/j.knosys.2023.110273","volume":"263","author":"W Saeed","year":"2023","unstructured":"Saeed, W., Omlin, C.: Explainable AI (XAI): a systematic meta-survey of current challenges and future opportunities. Knowl.-Based Syst. 263, 110273 (2023). https:\/\/doi.org\/10.1016\/j.knosys.2023.110273","journal-title":"Knowl.-Based Syst."},{"key":"3_CR72","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-030-44289-7_9","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)","author":"SA Salloum","year":"2020","unstructured":"Salloum, S.A., Alshurideh, M., Elnagar, A., Shaalan, K.: Mining in educational data: review and future directions. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) AICV 2020. AISC, vol. 1153, pp. 92\u2013102. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44289-7_9"},{"key":"3_CR73","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-030-28954-6_1","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"W Samek","year":"2019","unstructured":"Samek, W., M\u00fcller, K.-R.: Towards explainable artificial intelligence. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 5\u201322. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_1"},{"key":"3_CR74","unstructured":"Samek, W., Wiegand, T., M\u00fcller, K.-R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. ArXiv Preprint ArXiv:1708.08296 (2017)"},{"key":"3_CR75","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-030-88234-1_9","volume-title":"Re-imagining Educational Futures in Developing Countries: Lessons from Global Health Crises","author":"H Sharma","year":"2022","unstructured":"Sharma, H., Soetan, T., Farinloye, T., Mogaji, E., Noite, M.D.F.: AI adoption in universities in emerging economies: prospects, challenges and recommendations. In: Mogaji, E., Jain, V., Maringe, F., Hinson, R.E. (eds.) Re-imagining Educational Futures in Developing Countries: Lessons from Global Health Crises, pp. 159\u2013174. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-88234-1_9"},{"key":"3_CR76","doi-asserted-by":"publisher","first-page":"100730","DOI":"10.1016\/j.iheduc.2020.100730","volume":"46","author":"A Shibani","year":"2020","unstructured":"Shibani, A., Knight, S., Shum, S.B.: Educator perspectives on learning analytics in classroom practice. Internet High. Educ. 46, 100730 (2020)","journal-title":"Internet High. Educ."},{"key":"3_CR77","unstructured":"Tadepalli, P., Fern, X., Dietterich, T.: Deep reading and learning. OREGON STATE UNIV CORVALLIS CORVALLIS, USA (2017)"},{"key":"3_CR78","unstructured":"UNESCO. The promise of large-scale learning assessments: acknowledging limits to unlock opportunities. UNESCO (2019). https:\/\/unesdoc.unesco.org\/ark:\/48223\/pf0000369697"},{"issue":"3","key":"3_CR79","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s10994-018-5742-0","volume":"108","author":"V Vapnik","year":"2019","unstructured":"Vapnik, V., Izmailov, R.: Rethinking statistical learning theory: learning using statistical invariants. Mach. Learn. 108(3), 381\u2013423 (2019)","journal-title":"Mach. Learn."},{"key":"3_CR80","doi-asserted-by":"crossref","unstructured":"Veale, M., Van Kleek, M., Binns, R.: Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1\u201314 (2018)","DOI":"10.1145\/3173574.3174014"},{"key":"3_CR81","unstructured":"Vilone, G., Longo, L.: Explainable artificial intelligence: a systematic review. ArXiv Preprint ArXiv:2006.00093 (2020)"},{"key":"3_CR82","doi-asserted-by":"crossref","unstructured":"Walger, L., et al.: Artificial intelligence for the detection of focal cortical dysplasia: challenges in translating algorithms into clinical practice. Epilepsia (2023)","DOI":"10.1111\/epi.17522"},{"issue":"1","key":"3_CR83","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1080\/23752696.2021.1883458","volume":"6","author":"B Whalley","year":"2021","unstructured":"Whalley, B., France, D., Park, J., Mauchline, A., Welsh, K.: Towards flexible personalized learning and the future educational system in the fourth industrial revolution in the wake of Covid-19. High. Educ. Pedag. 6(1), 79\u201399 (2021)","journal-title":"High. Educ. Pedag."},{"key":"3_CR84","unstructured":"Woolf, B.P.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Morgan Kaufmann (2010)"},{"key":"3_CR85","doi-asserted-by":"crossref","unstructured":"Xia, X., Li, X.: Artificial intelligence for higher education development and teaching skills. Wirel. Commun. Mob. Comput. 2022 (2022)","DOI":"10.1155\/2022\/7614337"},{"key":"3_CR86","unstructured":"Xu, R., Baracaldo, N., Joshi, J.: Privacy-preserving machine learning: methods, challenges and directions. ArXiv Preprint ArXiv:2108.04417 (2021)"},{"key":"3_CR87","doi-asserted-by":"crossref","unstructured":"Yadav, A., et al.: A review of international models of computer science teacher education. In: Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education, pp. 65\u201393 (2022)","DOI":"10.1145\/3571785.3574123"},{"issue":"2","key":"3_CR88","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1108\/K-12-2020-0865","volume":"51","author":"MN Yakubu","year":"2022","unstructured":"Yakubu, M.N., Abubakar, A.M.: Applying machine learning approach to predict students\u2019 performance in higher educational institutions. Kybernetes 51(2), 916\u2013934 (2022)","journal-title":"Kybernetes"},{"issue":"1","key":"3_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41239-019-0171-0","volume":"16","author":"O Zawacki-Richter","year":"2019","unstructured":"Zawacki-Richter, O., Mar\u00edn, V.I., Bond, M., Gouverneur, F.: Systematic review of research on artificial intelligence applications in higher education\u2013where are the educators? Int. J. Educ. Technol. High. Educ. 16(1), 1\u201327 (2019)","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"3_CR90","unstructured":"Zeide, E.: Artificial intelligence in higher education: applications, promise and perils, and ethical questions. Educause Rev. 54(3) (2019)"},{"key":"3_CR91","doi-asserted-by":"publisher","first-page":"e8812542","DOI":"10.1155\/2021\/8812542","volume":"2021","author":"X Zhai","year":"2021","unstructured":"Zhai, X., et al.: A Review of artificial intelligence (AI) in education from 2010 to 2020. Complexity 2021, e8812542 (2021). https:\/\/doi.org\/10.1155\/2021\/8812542","journal-title":"Complexity"},{"issue":"5","key":"3_CR92","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3991\/ijet.v16i05.20307","volume":"16","author":"J Zhang","year":"2021","unstructured":"Zhang, J.: Computer assisted instruction system under artificial intelligence technology. Int. J. Emerg. Technol. Learn. (IJET) 16(5), 4\u201316 (2021)","journal-title":"Int. J. Emerg. Technol. Learn. (IJET)"},{"issue":"2","key":"3_CR93","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1631\/FITEE.1700053","volume":"18","author":"N Zheng","year":"2017","unstructured":"Zheng, N., et al.: Hybrid-augmented intelligence: collaboration and cognition. Front. Inf. Technol. Electron. Eng. 18(2), 153\u2013179 (2017). https:\/\/doi.org\/10.1631\/FITEE.1700053","journal-title":"Front. Inf. Technol. Electron. Eng."}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44067-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T06:02:43Z","timestamp":1707804163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44067-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440663","9783031440670"],"references-count":93,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44067-0_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/xaiworldconference.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"220","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}