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These constraints can limit instructors’ ability to deliver timely, personalized feedback while maintaining quality. Advances in AI-powered educational tools, including intelligent tutoring systems, automated assessment, and adaptive dashboards, can reduce instructional workload. This paper studies how explainable AI (XAI) can be embedded in teaching to strengthen professional development, reflection, and curricular and pedagogical design in higher education.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/explainable-ai-for-computer-science-instructors-enhancing-teaching-and-professional-practice/585333/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/explainable-ai-for-computer-science-instructors-enhancing-teaching-and-professional-practice/585333.png","ImageObject",300,407,{"name":92,"@type":93},"Aria Callaghan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"Which areas of AI in education are reviewed as foundations for the proposed approach?","Question",{"text":112,"@type":113},"The related work covers intelligent tutoring systems, learning analytics, and technology-supported formative assessment, highlighting issues of interpretability and actionability for instructors.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},585333,1792166491,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":135,"read_time":46,"preview_page_count":4},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","EXPLAINABLE AI FOR COMPUTER SCIENCE INSTRUCTORS: ENHANCING TEACHING AND PROFESSIONAL PRACTICE  \nMahmoud Mohanna  \nUniversité du Québec à Trois-Rivière, Québec, Canada  \nABSTRACT  \nTeaching computer science at undergraduate and graduate levels involves working with students ranging from coding novices to experienced programmers, large class sizes, and a shift toward online and hybrid classrooms. These conditions often prevent instructors from providing prompt, personalized feedback without compromising quality. Recent progress in AI-powered educational tools (Holstein & Aleven, 2019; Swamy et al., 2024), including intelligent tutoring systems (ITS), automated assessment, and adaptive dashboards, offers instructors practical support for lesson planning, teaching, and evaluation. This paper examines how Explainable AI (XAI) can be integrated into the teaching processes to reduce workload, enhance professional development, and support reflective practice. The proposed framework also emphasizes the role of collaborative faculty communities and considers broader implications for curriculum design, online pedagogy, and computer-assisted learning in higher education.  \nKEYWORDS  \nArtificial Intelligence, Explainable AI, Intelligent Tutoring Systems, Computer Science Education, Teacher Support, Professional Development  \n1. INTRODUCTION  \nArtificial intelligence (AI) is playing an increasingly visible role in higher education, especially in computer science courses at both the undergraduate and graduate levels. Instructors often face challenges such as large class sizes, students with diverse backgrounds, and the growing use of online and hybrid learning formats. Atthe same time, students increasingly expect timely, personalized feedback. Meeting these demands can make it challenging to maintain both depth and quality in teaching.  \nWe often hear about AI tutoring systems and automated grading platforms as answers to our teaching challenges. Most studies focus on how these tools help students, but this raises another important question: what do these tools offer instructors? When AI systems can explain their reasoning, a concept known as explainable AI (XAI), they offer even greater value. Instead of just handling routine tasks, they can help us better understand what’s happening in our classrooms and give us more time to focus on the kind of teaching we care about (Holstein & Aleven, 2019) . Beyond easing workload, XAI can also enhance professional development and collaboration by making teaching processes more transparent and easier to share (Buckingham Shum et al., 2019; Altukhi & Pradhan, 2025) .  \nThis paper explores how XAI can be integrated into computer science instruction to improve instructor support and professional growth. Drawing on teaching experiences in areas such as algorithms, programming, and systems, the paper shows how AI explanations can be used not only to clarify student reasoning but also to inform curriculum design and instructional strategies. The discussion is organized into five sections. Section 2 reviews related work on AI in education and the emerging role of XAI in teaching contexts. Section 3 introduces a framework for integrating XAI into instructor workflows, focusing on curriculum design, delivery, and reflective practice. Section 4 illustrates these ideas with case-based insights from computer science courses. Section 5 examines implications for professional development, faculty collaboration, and higher education policy. Finally, Section 6 concludes with reflections on future directionsand the potential of XAI to enhance instructional quality and institutional innovation.  \n2. RELATED WORK  \nResearch on AI in education spans several connected fields. In this review, I looked at work published over the past decade using ACM Digital Library, IEEE Xplore, and ERIC. I focused on studies about explainable AI, intelligent tutoring systems, learning analytics, and teacher support. From the set of papers ","cbCaih9kGBSuMM2v","https://ap.wps.com/l/cbCaih9kGBSuMM2v","pdf",727201,"English","# 1. Introduction\n## Artificial intelligence in higher education and instructor needs\n# 2. Related Work\n## 2.1 Intelligent Tutoring Systems (ITS)\n## 2.2 Learning Analytics\n# 3. Framework for Integrating XAI into Instructor Workflows\n## Curriculum design, delivery, and reflective practice\n# 4. Case-Based Insights from Computer Science Courses\n# 5. Implications for Professional Development and Policy\n# 6. Conclusion and Future Directions","[{\"question\":\"Which areas of AI in education are reviewed as foundations for the proposed approach?\",\"answer\":\"The related work covers intelligent tutoring systems, learning analytics, and technology-supported formative assessment, highlighting issues of interpretability and actionability for instructors.\"}]","EXPLAINABLE AI FOR COMPUTER SCIENCE INSTRUCTORS - Enhancing Teaching and Professional Practice | PDF",1792054407]