Since teaching is the foundation of education, program accreditation and institutional performance are directly correlated with its effectiveness. By creating a competitive and supportive learning environment, faculty performance has a direct impact on an academic institution’s ability to fulfill its vision and goal. To provide a thorough and impartial assessment of teaching performance, this study uses data mining algorithms to extract insightful information about the elements that go into good instruction, including both structured and unstructured data. This is done in response to the urgent need for faculty performance evaluation. To help institutions identify their strengths, rectify their flaws, and encourage ongoing growth in their teaching and learning processes, the system was created. Looking for trends in teacher data. Furthermore, sentiment analysis methods are employed to assess qualitative input, and Laravel 8.0 provides the framework for putting these algorithms into practice. A grand mean score of 4.38, which is considered “Very Acceptable,” was obtained from expert evaluations of the system, demonstrating its dependability and efficacy in assisting with faculty performance reviews.

This chapter establishes the core foundations of upskilling and reskilling in an AI-driven workplace and discusses their importance in workforce innovation. Ongoing education and continuous learning are highlighted as key factors in making workers responsive to changing industry needs. Through the integration of industry and educational institution case study insights, such as Siemens lifelong learning initiatives, the Center for Integrated STEM Education–Massachusetts Institute of Technology Responsible AI for Social Empowerment and Education (CISTEM-MIT RAISE) AI literacy initiatives, and the Miriam College Technology Business Incubator (MC-TBI), the discussion emphasizes how workforce strategies powered by AI improve skills development, increase productivity, and establish sustainable career paths. Predictive workforce analytics, adaptive learning pathways, and industry-specific training methodologies are examined as high-impact interventions for mitigating skill deficiencies. The transforming character of learning institutions and training schemes is also considered, with special emphasis placed on the necessity for academia–industry–government partnerships for the creation of scalable and accessible learning ecosystems. The findings reveal that AI-supported reskilling initiatives, backed by established assessment methodologies and policy frameworks, significantly improve workforce adaptability and long-term employment prospects, promoting ongoing learning environments, accessibility-focused training solutions, and collaborative partnerships to future-proof workforce in the digital economy.

Mathematics is a subject that often feels distant from everyday life, yet its logic quietly shapes the world around us. As learners continue to question its practical significance, there is a growing need to rethink mathematics education. Recently, the emergence of artificial intelligence (AI) has opened new possibilities for transforming how mathematics is taught and learned. This chapter aims to examine the emerging role of AI in mathematics education by synthesizing current tools, identifying prevailing trends, and exploring transformative applications. Using a narrative review supported by expert-informed synthesis, several themes are identified that reflect how AI are reshaping instructional practices, learner engagement, and pedagogical design. The discussion integrates illustrative examples of AI tools to highlight their instructional relevance and underlying mechanisms. The chapter concludes by reflecting on a redefined landscape for mathematics education, where technology transforms instructional practices, learner experiences, and the development of mathematical thinking.

The construction sector is a substantial generator of waste and carbon dioxide emissions worldwide. The use of sustainable materials in construction could minimize its negative effects on the environment. This research is intended to offer a soft computing model for predicting the deformation and parallel bending strength (PBS) of Guadua angustifolia applying an artificial neural network (ANN)-particle swarm optimization (PSO) approach and employing the data obtained from the experimental tests performed in the study. The input parameters (IP) utilized in the modeling process include the outside diameter, wall thickness, minimum length, external taper, perpendicular distance of bow, ISO ovality, eccentricity, actual shear span, area, modulus of elasticity, density, and linear mass. The resulting models showed R values of 0.99076 and 0.99976 and MAPE of 0.936% and 0.345% for deformation and PBS, respectively. The findings of the sensitivity analysis (SA) also exhibited that ISO ovality and eccentricity were the most important parameters to the deformation and PBS models. Research outcomes demonstrated the effectiveness of the ANN-PSO approach for predicting the deformation and parallel bending strength characteristics of Guadua angustifolia. The modeling approach proposed in this study could be utilized for speeding up the material characterization phase of similar construction materials.

This chapter explores the developmental implications of artificial intelligence (AI) in contemporary educational settings. Employing a dual-methodological approach that combines interdisciplinary expertise with an integrative literature review, the chapter explores how AI technologies are reshaping student identity, emotional regulation, motivation, autonomy, and interpersonal relationships. Drawing on developmental psychology, educational theory, and empirical research, it interrogates the unintended consequences of algorithmic surveillance, pedagogical automation, and AI-mediated social interactions. The analysis highlights both the affordances and risks of AI in education, including its impact on emotional resilience, self-directed learning, and the construction of academic identity. Reframing the discourse away from purely technical efficiency toward developmental integrity makes visible the deeper human stakes of AI integration in education. The chapter consequently calls for ethical, inclusive, and human-centered approaches to AI design and implementation in schools.

Once confined to science fiction and speculative futures, generative artificial intelligence (GenAI) has swiftly entered the lecture halls of modern medical education. Despite its expanding use, a synthesis of its implementation, limitations, and educational value remains underexplored. This review aims to critically examine current applications, identify pedagogical pitfalls, and delineate future trajectories for GenAI in medical training. Key innovations include AI-driven content generation tailored to curricular benchmarks, automated assessments with real-time diagnostic feedback, and immersive virtual patient simulations replicating complex pathophysiologies. Additional advances span multilingual knowledge translation, anatomically precise surgical training environments, and adaptive learning systems powered by intelligent tutoring frameworks. As discussed herein, GenAI holds transformative potential for advancing clinical competence in an evolving medical landscape—provided its integration is evidence-based, ethically sound, and educationally coherent.

In an era marked by rapid technological advancement, protecting the intellectual property (IP) of educational innovations has become more critical than ever. This chapter examines the intersection of educational innovation, artificial intelligence (AI), and IP protection. Patents, which safeguard the technical and functional aspects of inventions, are crucial for protecting these advancements amid rapid technological disruptions. As discussed in the chapter, several challenges are posed by AI in generating and managing IP, including the need to redefine inventorship, address skill obsolescence, and ensure equitable IP frameworks. Despite the importance of addressing these issues to foster innovation, they remain underexplored in the existing literature. Therefore, this chapter calls for a reassessment of existing legal and procedural frameworks to adapt to the evolving IP landscape and sustain the integrity of educational innovations. Overall, this chapter aims to contribute to the development of robust strategies for safeguarding educational innovations in an AI-driven era.

As artificial intelligence (AI) becomes increasingly integrated into educational contexts, they present new challenges to traditional assessment methods. A particularly pressing issue is academic dishonesty, which undermines learning authenticity and the credibility of educational institutions. With generative AI tools like ChatGPT making it easier for students to produce automated answers, educational assessments are at risk of measuring AI capabilities rather than students' actual knowledge. Thus, this chapter explores a range of strategies designed to adapt assessment practices in response to the influence of AI in education. These strategies offer actionable frameworks to support authentic learning and uphold academic integrity. Additionally, the chapter highlights future research directions to guide further adaptation of educational policies and practices. Given the rapid integration of AI in the education sector, this chapter provides sensible insights that reinforce the importance of integrity-focused reforms in sustaining meaningful educational outcomes in an AI-driven world.

As generative AI tools become increasingly integrated into educational practice, its use among pre-service teachers is often accompanied by hesitation and discomfort. This chapter examines the phenomenon of AI shaming among teacher education students—the stigma and reluctance to disclose AI tool use due to perceived threats to academic authenticity. Drawing on classroom insights and student reflections, it explores how social norms, institutional pressures, and identity formation shape this behavior. These experiences reveal the deep tension between embracing technological innovation and maintaining traditional standards of academic merit. The chapter highlights the implications for digital literacy, professional development, and ethical technology integration. It calls for a shift in narrative, framing AI not as a shortcut but as a tool for innovation. Actionable strategies for educators and institutions are proposed to foster open, reflective, and supportive environments for responsible AI use in teacher education.

The integration of artificial intelligence (AI) into engineering education has emerged as a transformative force, offering innovative tools to enhance teaching, learning, and administrative processes. This study presents a systematic review of the current landscape, focusing on the AI technologies application, the regulatory frameworks, and the challenges encountered in engineering education. The findings reveal how AI can improve student learning outcomes, personalize educational experiences, and automate complex processes. The review also addresses critical issues, such as ethical considerations and the imperative for regulatory compliance. Furthermore, it identifies key barriers to adoption, such as technological limitations and the preparedness of educators and students to embrace AI-powered solutions. This study provides a comprehensive understanding of the potential and limitations of AI in engineering education, offering actionable insights for educators, policymakers, and stakeholders aiming to foster effective and ethical AI integration in academic settings.