The advancement of computational Artificial Intelligence (AI) in the recent decade has been transformative for many domains, including AI in Education. One direction, where it has caused a noticeable increase in research activity, is application of AI technologies to enhance digital textbooks by making them more interactive, engaging, adaptive, and intelligent. For many researchers coming into this field, it would have seemed as if an intelligent textbook is a completely new idea. We would like to provide a historic outlook on this field and outline the important phases that it went through over the last three decades. We hope that such an account can inform interested readers and help them better understand the problems and the approaches of intelligent textbooks

Brusilovsky, Peter

Sosnovsky, Sergey

Thaker, Khushboo

Utrecht University Repository

Received: 30 September 2021 Accepted: 30 September 2021DOI: 10.1002/aaai.12061COLUMNThe return of intelligent textbooksPeter Brusilovsky1 Sergey Sosnovsky2 Khushboo Thaker11Intelligent Systems Program, School ofComputing and Information, Universityof Pittsburgh, Pittsburgh, Pennsylvania,USA2Department of Information andComputing Sciences, Utrecht University,Utrecht, The NetherlandsCorrespondenceKhushboo Thaker, Intelligent SystemsProgram, School of Computing andInformation, University of Pittsburgh,4200 Fifth Avenue, Pittsburgh, PA, USA.Email: k.thaker@pitt.eduAbstractThe advancement of computational Artificial Intelligence (AI) in the recentdecade has been transformative for many domains, including AI in Education.One direction, where it has caused a noticeable increase in research activity, isapplication ofAI technologies to enhance digital textbooks bymaking themmoreinteractive, engaging, adaptive, and intelligent. For many researchers cominginto this field, it would have seemed as if an intelligent textbook is a completelynew idea.Wewould like to provide a historic outlook on this field and outline theimportant phases that it went through over the last three decades. We hope thatsuch an account can inform interested readers and help them better understandthe problems and the approaches of intelligent textbooks.INTRODUCTIONIn the new millennium, we are witnessing a quiet revo-lution in the area of textbooks, the traditional tools forlearning. On one hand, digital textbooks are graduallyreplacing their printed counterparts. On the other hand,researchers frommany fields of AI consider the emergenceof a digital textbook as an opportunity to apply varioustechnologies that could further augment them. Over thelast 10 years, the research on intelligent textbooks, thatis, the use of AI to enrich textbooks, has become one ofthe leading streams in this area. However, this is rathera “return of intelligent textbooks” since the research onapplying AI to textbooks startedmore than 30 years ago. Inthis paper, we make an attempt to show the “big picture”of research on intelligent textbooks tracing the historyof this work from the 1990s to nowadays. We also try totrace the generations of AI technologies that contributed tothe field.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided theoriginal work is properly cited.© 2022 The Authors. AI Magazine published by Wiley Periodicals LLC on behalf of the Association for the Advancement of Artificial Intelligence.KNOWLEDGE ENGINEERINGGENERATIONThe first intelligent textbooks were developed in 1990 inthe field of adaptive hypermedia. The intelligence andpersonalization in these books were based on classic AIapproach—knowledge engineering produced by domainexperts. For every textbook, domain experts determinedkey concepts presented in it (domain model) and anno-tated textbook fragments with sets of concepts coveredthere (content model).The knowledge of individual learners (student model)was represented dynamically as weighed overlaysof the domain model. With relatively simple mod-els, early intelligent textbooks pioneered a range of“smart” functionalities-such as adaptive navigation,adaptive presentation, content recommendation, andconcept-based navigation (Henze et al. 1999; Weberand Brusilovsky 2001). More recent work based onAI Magazine. 2022;43:337–340. wileyonlinelibrary.com/journal/aaai 337338 AI MAGAZINEF IGURE 1 The evolution of intelligent textbook technologiesexpert-driven knowledge engineering used a finer levelknowledge representation that also enabled questionanswering (Chaudhri et al. 2013). While this generation ofintelligent textbooks demonstrated what could be done byrepresenting “knowledge behind pages,” the reliance onexpensive knowledge engineering made these approacheshard to scale.SEMANTICWEB GENERATIONIn the beginning of 2000s, the attention of many AIresearchers shifted towards Semantic Web (SW) tech-nologies. Standardized formats and modeling languagesfor shareable and expressive knowledge representationwere introduced along with new architectural solutionsfor intelligent software. This development resonated withthe intelligent textbooks community. Implementation ofknowledge models as SW ontologies and educationalmaterial as linked and annotated learning objects openedup adaptive textbooks architectures and enabled integra-tion of external content. Initial projects developing theseideas focused on proposing new fully ontological architec-tures that allowed manual extension of adaptive textbookswith external learning objects (Dolog et al. 2004; Meliset al. 2006). Later, successful attempts were made onapplying SW technologies to automate integration of exter-nal content into adaptive textbooks (Sosnovsky, Hsiao,and Brusilovsky 2012). Another direction of research onSW-empowered textbooks has stemmed from the expres-sive power of descriptive logic combined with symbolicnatural language processing (NLP). It has supported devel-opment of semantically annotated textbook prototypesthat “understand” their content and can engage in richinteractions with students including meaningful questionanswering and concept mapping exercises (Chaudhri et al.2013).NLP +ML GENERATIONFurther advancement in the field of NLP and machinelearning (ML) enabled a new generation of research onusing the very content and structure of textbooks as asource of knowledge for “smart” functionality. A numberof papers explored approaches for automatic extraction oftopics and concepts from textbooks (Wang et al. 2016) aswell as to mine various kinds of semantic relationshipsbetween these concepts (Labutov et al. 2017). Ultimately,this line of research leads to automatic construction of fullconcept-based domain models “behind pages” (Alpizar-Chacon and Sosnovsky 2021). These models can be usedto support intelligent and adaptive services pioneered bythe early textbooks (such as concept-based navigationor integration of external content), yet without expen-sive manual knowledge engineering. Another applicationof NLP technologies that has gained prominence in thelast several years is the use of textbooks to generateadditional learning content such as assessment ques-tions (Kumar, Banchs, and D’Haro 2015) or definitions(Yarbro and Olney 2021).INTERACTION DATAMININGGENERATIONThe next advancement in intelligent textbooks has beenfueled by the growing volume of user interaction data gen-erated from digital textbooks and modern technologiesthat leverage these data in several directions (Figure 1).For example, interaction data has been used to improverecognition of prerequisite relationships between domainconcepts (Chaplot et al. 2016). Data generated by page nav-igation and highlighting behavior were used to mine stu-dents’ reading strategies and track their focus of attention(Yogev et al. 2018; Winchell et al. 2018). 23719621, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aaai.12061 by Utrecht University Library, Wiley Online Library on [27/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons LicenseAI MAGAZINE 339More recently, interaction data were applied to inferthe knowledge state of students’ reading textbooks. Tra-ditional student modeling technologies, which use stu-dent problem-solving as evidence of knowledge growth,were redesigned to use users’ reading behavior like pagenavigation and dwell time. This enhances the qualityof students’ knowledge modeling in digital textbooksand supports maintaining knowledge models for text-books where student practice evidence is not avail-able (Thaker et al. 2018; Carvalho et al. 2018). Ulti-mately, advanced domain modeling and the improvedstudent modeling enabled more powerful personaliza-tion approaches for digital textbooks platforms to enableadaptive recommendations (Thaker et al. 2020) basedon students real-time behavior for advance reading andremediation.While we titled this paper “The Return of IntelligentTextbooks” to stress an increased interest to this topic overthe last few years, our brief review shows that the workon intelligent textbooks has never stopped. It moved onwith every new generation of AI technologies, exploringnew exciting ways to make textbook “smart,” interactiveand useful. We expect that this trend will continue andhope that this brief review will be helpful to inform thefuture work.CONFL ICT OF INTERESTThe authors declare that there is no conflict.ORCIDKhushbooThaker https://orcid.org/0000-0003-3619-9376REFERENCESAlpizar-Chacon, I., and S. Sosnovsky. 2021. “KnowledgeModels fromPDFTextbooks.”NewReview ofHypermedia andMultimedia 27(1):1–49.Carvalho, P., M. Gao, B. Motz, and K. Koedinger. 2018. “Analyzingthe Relative Learning Benefits of Completing Required Activitiesand Optional Readings in Online Courses.” In Proceedings of theEleventh International Conference on Educational Data Mining,418–23. Buffalo, NY, USA: International Educational Data MiningSociety.Chaplot, D., Y. Yang, J. Carbonell, and K. Koedinger. 2016. “Data-driven Automated Induction of Prerequisite Structure Graphs.” InProceedings of the Ninth International Conference on EducationalDataMining, 318–23. Raleigh, NC, USA: Educational DataMiningSociety.Chaudhri, V., B. Cheng, A. Overholtzer, J. Roschelle, A. Spaulding,P. Clark, M. Greaves, and D. Gunning. 2013. “Inquire Biology: ATextbook that Answers Questions.” AI Magazine 34(3): 55–72.Dolog, P., N. Henze, W. Nejdl, and M. Sintek. 2004. “The PersonalReader: Personalizing and Enriching Learning Resources UsingSemantic Web Technologies.” In Proceedings of the InternationalConference on Adaptive Hypermedia and Adaptive Web-BasedSystems, 85–94. Berlin, Heidelberg: Springer.Henze, N., K. Naceur, W. Nejdl, and M. Wolpers. 1999. “AdaptiveHyperbooks for Constructivist Teaching.” Künstliche Intelligenze13(4): 26–31.Kumar, G., R. Banchs, and L. D’Haro. 2015. “RevUP: Automatic Gap-fill Question Generation from Educational Texts.” In Proceedingsof the Tenth Workshop on Innovative Use of NLP for Building Edu-cational Applications, 154–61. Denver, CO, USA: Association forComputational Linguistics.Labutov, I., Y. Huang, P. Brusilovsky, and D. He. 2017. “Semi-supervised Techniques for Mining Learning Outcomes andPrerequisites.” In Proceedings of the 23rd ACM SIGKDD Inter-national Conference on Knowledge Discovery and Data Min-ing, 907–15. New York, NY, USA: Association for ComputingMachinery.Melis, E., G. Goguadze, M. Homik, P. Libbrecht, C. Ullrich, and S.Winterstein. 2006. “Semantic-aware Components and Services ofActivemath.”British Journal of Educational Technology 37(3): 405–23.Sosnovsky, S., I. Hsiao, and P. Brusilovsky. 2012. “Adaptation “in thewild”: Ontology-based Personalization of Open-corpus LearningMaterial.” In Proceedings of the Seventh European Conference ofTechnology Enhanced Learning, 425–31. Saarbrücken, Germany:Springer.Thaker, K., Y. Huang, P. Brusilovsky, and H. Daqing. 2018. “DynamicKnowledge Modeling with Heterogeneous Activities for AdaptiveTextbooks.” In Proceedings of the Eleventh International Con-ference on Educational Data Mining, 592. Buffalo, NY, USA:International Educational Data Mining Society.Thaker, K., L. Zhang, D. He, and P. Brusilovsky. 2020. “Recom-mending Remedial Readings using Student’s Knowledge State.”In Proceedings of the Thirteenth International Conference on Edu-cational Data Mining, 233–44. International Educational DataMining Society.Wang, S., A. Ororbia, Z. Wu, K. Williams, C. Liang, B. Pursel,and L. Giles. 2016. “Using Prerequisites to Extract Concept Mapsfrom Textbooks.” In Proceedings of the Twenty-Fifth ACM Inter-national on Conference on Information and Knowledge Manage-ment, 317–26. Indianapolis, IN, USA: Association for ComputingMachinery.Weber, G., and P. Brusilovsky. 2001. “ELM-ART: An Adaptive Ver-satile System for Web-based Instruction.” International Journal ofArtificial Intelligence in Education 12(4): 351–84.Winchell, A., M. Mozer, A. Lan, P. Grimaldi, and H. Pashler. 2018.“Textbook Annotations as an Early Predictor of Student Learn-ing.” In Proceedings of the Eleventh International Conference onEducational DataMining, 431–37. Buffalo, NY, USA: InternationalEducational Data Mining Society.Yarbro, J. and A. Olney. 2021. “Contextual Definition Genera-tion.” In Proceedings of the Third International Workshop onIntelligent Textbooks Co-located with Twenty-Second Interna-tional Conference on Artificial Intelligence in Education, 74–83.CEUR-WS.org.Yogev, E., K. Gal, D. Karger, M. Facciotti, and M. Igo. 2018. “Classi-fying and Visualizing Students’ Cognitive Engagement in CourseReadings.” In Proceedings of the Fifth Annual ACM Conferenceon Learning at Scale, 1–10. New York, NY, USA: Association forComputing Machinery. 23719621, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aaai.12061 by Utrecht University Library, Wiley Online Library on [27/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License340 AI MAGAZINEAUTH OR BIOGRAPH IESPeter Brusilovsky is a Professorof Information Science and Intel-ligent Systems at the University ofPittsburgh, where he directs Per-sonalized Adaptive Web Systems(PAWS) lab. Peter has been work-ing in the field of personalizedlearning, student and user modeling, recommendersystems, and intelligent user interfaces for over 30years. He published numerous papers and edited bookson adaptive hypermedia, and the adaptive Web, andsocial information access. His current interests arefocused on user control in adaptive learning, recom-mender systems, and personalized health systems. Hecan be reached at peterb@pitt.edu.Sergey Sosnovsky is an Assis-tant Professor of Software Tech-nology for Learning and Teachingat the Department of Informationand Computing Sciences, UtrechtUniversity. His research interestsinclude various aspects of design-ing, developing and evaluating adaptive educationalsystems and personalized information systems in gen-eral. He is an organizer of the workshop series onIntelligent Textbooks conducted yearly in conjunc-tion with the International Conference on Artifi-cial intelligence in Education. He can be reached ats.a.sosnovsky@uu.nl.Khushboo Thaker is a PhDstudent of the Intelligent Sys-tems Program at the Universityof Pittsburgh. Khushboo is alsoa graduate research scholarworking for Health e-Librarianwith Personalized Recommender(HELPeR) project. Previously she worked as a researchassociate at Indian Institute of Science, Bangalore.Her dissertation focuses on building user knowledgemodels for adaptive and personalized recommendersystems. Her previous research includes domaininformation extraction and representation. She can bereached at k.thaker@pitt.edu.How to cite this article: Brusilovsky, P., S.Sosnovsky, and K. Thaker. 2022. “The return ofintelligent textbooks.” AI Magazine 43: 337–40.https://doi.org/10.1002/aaai.12061 23719621, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aaai.12061 by Utrecht University Library, Wiley Online Library on [27/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

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