Explainable AI for STEAM Personalization and Ethical Assessment

Artificial intelligence is rapidly transforming educational environments, creating new opportunities to personalize learning, enhance student engagement, and support more effective assessment practices. Within Science, Technology, Engineering, the Arts, and Mathematics (STEAM) education, intelligent systems are increasingly being used to adapt learning experiences to individual needs, provide real-time feedback, and generate insights that help educators better support student success. At the same time, the growing use of AI in educational decision-making has raised important concerns regarding transparency, fairness, accountability, and trust. As learning environments become more data-driven and technologically sophisticated, there is a pressing need to ensure that intelligent systems remain understandable, ethical, and aligned with educational goals. Explainable Artificial Intelligence (XAI) has emerged as a promising approach for addressing these challenges by making AI-driven processes more transparent and interpretable for learners, educators, and institutions. Explainable AI for STEAM Personalization and Ethical Assessment examines the role of explainable and trustworthy AI technologies in shaping the future of smart education. Emphasizing both innovation and responsibility, this book highlights the importance of transparency, ethics, and human-centered design in the development of AI-enabled educational ecosystems. Through theoretical perspectives, empirical research, system frameworks, and practical applications, this book contributes to the advancement of intelligent learning environments that support both educational excellence and ethical decision-making. Covering topics such as dropout risk assessment, adaptive recommender systems, and verifiable synthetic generation, this book is an excellent academic resource for graduate and doctoral students, school leaders, curriculum designers, instructional technologists, assessment specialists, educational evaluators, AI developers, software engineers, policymakers, and more.

Jorge Ruiz Vanoye is a Professor and Researcher at Universidad Politécnica de Pachuca (UPP), a National Researcher under the Secretaria de Ciencia, Humanidades, Tecnología e Innovación program, and an Adjunct Researcher at the National Laboratory of Autonomous Vehicles and Exoskeletons (LANAVEX). Additionally, he holds the title of Lecturer-level Researcher for the Agency for the Quality of the University System of Catalonia (Generalitat de Catalunya). His research interests include Smart Cities, Smart Water, Smart Education, Smart Government, Smart Healthcare, Smart Farming, Smart Energy, Smart Sports, Smart Food, Smart Tourism, Algorithmic Finance, Algorithmic Portfolio Management, Applications and Theory of Algorithms, Bio-inspired Algorithms, Combinatorial Optimization Problems, Compilers, Computational Intelligence, Computational Complexity Theory, Computational Financial Intelligence, Complexity of Algorithms, Complexity of Instances, Computational Statistics, Computer Networks, Computer Science Security, Cybercrimes, Data Mining, Education, Evolutionary Computation, Heuristic Optimization Techniques in Bioinformatics, Hybrid Evolutionary Algorithms, Hybrid Optimization Algorithms, Machine Learning, Meta-heuristics, Operations Research, Operations Management, Parallel & Distributed Computing, Production Planning and Logistics Optimization, Project Scheduling Problems, Software Engineering, and Transgenic Algorithms.

Francisco R. Trejo-Macotela received the PhD degree in Electronics from the National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He is a Certified LabVIEW Associate Developer (CLAD) accredited by National Instruments and holds professional competence certifications in the EC0477 and EC0217 standards issued by the National Council for Standardisation and Certification of Competencies (CONOCER). He is currently Professor and Researcher in the Master's programmes in Mechatronics and Information and Communication Technologies, and the PhD programme in Advanced Science and Technology, at the Polytechnic University of Pachuca (UPP), Mexico. During his academic career at UPP, he has held several leadership positions, including Director of Mechatronics Engineering (2007–2009), Director of Research, Innovation and Postgraduate Studies (2015–2023), and Coordinator of the Master's Programme in Information and Communication Technologies (2023–2024). He also served as Professor and Researcher in the Departments of Mechatronics (2006–2007) and Telematics (2009–2015). He has contributed to numerous research and innovation projects and has participated in more than eight evaluation processes for master's and doctoral programmes within the National Postgraduate System (SNP) of the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI). He is an Adjunct Researcher at the SECIHTI National Laboratory for Autonomous Vehicles and Exoskeletons (LANAVEX). He currently serves as an evaluator for the Interinstitutional Committees for the Evaluation of Higher Education (CIEES), the Engineering Education Accreditation Council (CACEI–COPAES), and the Higher Education Evaluation and Accreditation System (SEAES) through the Peer Registry for Diagnostic and Formative Evaluation (REPEV) of the Mexican Ministry of Public Education (SEP). In 2019, he served as an External Peer Evaluator for the accreditation of Ecuadorian universities and polytechnic institutions under the Higher Education Quality Assurance Council (CACES). He has also acted as a peer evaluator for postgraduate programmes within the National Postgraduate System (SNP) and as a project evaluator for the Innovation Incentives Programme (PEI) of SECIHTI. He is currently a Level 1 member of the National System of Researchers (SNI) of SECIHTI. His research interests include Integrated Circuit Design, Analogue and Digital Electronics, Automation, Embedded Systems, Cyber-physical Systems, Internet of Things, Telecommunications, Smart Cities, Smart Water, Precision Agriculture, Educational Technology, Artificial Intelligence, Machine Learning, Autonomous Navigation, and Optimisation.