This concluding chapter explores how artificial intelligence (AI) is reshaping the purposes, practices, and outcomes of science education, and proposes a human-centered framework for its responsible integration. Drawing on insights from international collaborations and the Advancing AI in Science Education (AASE) committee, the chapter synthesizes developments across five dimensions: educational goals, instructional procedures, learning materials, assessment, and outcomes. We argue that AI offers transformative potential to enrich inquiry, personalize learning, and support teacher practice, but only when guided by Responsible and Ethical Principles (REP). The REP framework, emphasizing fairness, transparency, privacy, accountability, and respect for human values, anchors our vision for AI-supported science education. Key discussions include the redefinition of scientific literacy to encompass AI literacy, the evolving roles of teachers and learners in AI-supported classrooms, and the design of adaptive learning materials and assessments that preserve authenticity and integrity. We highlight both opportunities and risks, stressing the need for critical engagement with AI to avoid reinforcing inequities or undermining human agency. Ultimately, this chapter advances a vision in which science education prepares learners to act as ethical investigators and responsible citizens, ensuring that AI innovation aligns with human dignity, equity, and the broader goals of scientific literacy.
We study education as a remedy for misspecified beliefs in a canonical sequential social-learning model. Uneducated agents misinterpret action histories - treating actions as if they were independent signals and, potentially, overstating signal precision - while educated agents use the correct likelihoods (and may also enjoy higher private precision). We define a misspecified-belief PBE and show existence with a simple structure: education is a cutoff in the realized cost and actions are threshold rules in a single log-likelihood index. A closed-form value-of-education statistic compares the accuracy of the educated versus uneducated decision at any history; this yields transparent conditions for self-education. When a misspecified process sustains an incorrect cascade, uniformly positive private value and a positive flip probability imply that education breaks the cascade almost surely in finite time, with an explicit bound on expected break time. We quantify welfare gains from making education available and show how small per-education subsidies sharply raise de-cascading probabilities and improve discounted welfare. Extensions cover imperfect observability of education choices and a planner who deploys history-dependent subsidies.
Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality. We discuss what computer science educators should teach in the AI era, how to best integrate these technologies into curricula, and the best practices for assessing student learning in an environment where AI can generate code, prototypes and user feedback. Finally, we propose a set of policy recommendations designed to harness the potential of generative AI while preserving the integrity and rigour of computer science education. Empirical data and emerging studies are used throughout to support our arguments.
As Artificial Intelligence (AI), particularly Large Language Models (LLMs), becomes increasingly embedded in education systems worldwide, ensuring their ethical, legal, and contextually appropriate deployment has become a critical policy concern. This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions, including the European Union, United Kingdom, United States, China, and Gulf Cooperation Council (GCC) countries. It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures. Special emphasis is placed on the evolving landscape in the GCC, where countries are rapidly advancing national AI strategies and education-sector innovation. To support this development, the paper introduces a Compliance-Centered AI Governance Framework tailored to the GCC context. This includes a tiered typology and institutional checklist designed to help regulators, educators, and developers align AI adoption with both international norms and local values. By synthesizing global best practices with region-specific challenges, the paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education. These insights are intended to inform future regulatory harmonization and promote responsible AI integration across diverse educational environments.
Analyzing students' written solutions to physics questions is a major area in PER. However, gauging student understanding in college courses is bottlenecked by large class sizes, which limits assessments to a multiple-choice (MC) format for ease of grading. Although sufficient in quantifying scientifically correct conceptions, MC assessments do not uncover students' deeper ways of understanding physics. Large language models (LLMs) offer a promising approach for assessing students' written responses at scale. Our study used an LLM, validated by human graders, to classify students' written explanations to three questions on the Energy and Momentum Conceptual Survey as correct or incorrect, and organized students' incorrect explanations into emergent categories. We found that the LLM (GPT-4o) can fairly assess students' explanations, comparable to human graders (0-3% discrepancy). Furthermore, the categories of incorrect explanations were different from corresponding MC distractors, allowing for different and deeper conceptions to become accessible to educators.
L’introduzione, a partire dall’anno scolastico 2020-2021, del giudizio descrittivo quale modalità di valutazione periodica e finale degli apprendimenti nella Scuola Primaria (L. 6 giugno 2020, n. 41) rappresenta un passo significativo verso l'integrazione delle pratiche della valutazione degli apprendimenti e delle competenze.
Prendendo le mosse dagli orientamenti del Quadro Europeo delle Qualificazioni EQF, la valutazione assume prospettive diverse dalla sola di attività di verifica e accertamento. Essa si basa sulla descrizione dettagliata delle abilità e delle conoscenze acquisite dagli alunni, mettendo in evidenza il processo di apprendimento e le competenze sviluppate assumendo una visione olistica dell'apprendimento. Nell’elaborazione del giudizio, infatti, non sono sufficienti gli esiti di apprendimento in rapporto agli obiettivi, ma è necessario considerare le tappe e i processi che gli studenti attraversano durante tutto il percorso.
Introducing a new technology can be very exciting especially to the technology creators. However, as espoused by the Technology Acceptance Model (TAM), while the creator of a given technology may believe that the new technology is very user-friendly, it may only be accepted by its potential users if they share the same beliefs. The study sought to establish strategies that Open, Distance, and eLearning Centers pursue to enhance acceptance of new technologies by online learners. This study is mainly anchored on the Technology Acceptance Model (TAM) which is widely used to explain the factors related to users’ acceptance of new technologies. A cross-sectional survey of seventy-three (73) universities in Kenya was undertaken with the in-charge Open, Distance, and electronic Learning (ODel) Centers as the respondents. Fifty-nine (59) respondents completed a survey tool, which was shared as a Google form. Data were analysed quantitatively and qualitatively. Continuous evaluation and improvement of ODeL technologies was the most common strategy perceived to enhance new technology acceptance at 85%. The study recommends a need to develop and operationalise a new technology acceptance and adoption policy as a key strategy to enhance the acceptance of new ODeL technologies.
El cuerpo académico asume la responsabilidad de impulsar las innovaciones educativas de acuerdo con los lineamientos planteados en los modelos educativos de las instituciones de educación superior. En este sentido, un modelo curricular basado en competencias requiere de un modelo didáctico pertinente y coherente a la formación profesional. La investigación tiene como propósito proponer un plan didáctico validado para la orientación de la formación profesional de ingenieros de una institución de educación superior del sur de Chile. Se utilizó un enfoque mixto con un método de investigación basada en diseño, lo cual implicó tres fases: definir, construir y validar. Para el proceso de definición se utilizó una revisión sistemática de la literatura basada en PRISMA, en la que se analizaron un total de 85 artículos en la que se determinaron las metodologías más utilizadas para la enseñanza de la ingeniería, como el aprendizaje basado en proyectos, simulación, gamificación, aprendizaje basado en problemas. En la fase de construcción se consideró un modelo constructivista o alternativo-investigativo, debido a que centra el aprendizaje en el estudiantado quien por medio de problemas construye su conocimiento. Se organizaron las metodologías activas de acuerdo con el tipo de escenario de aprendizaje. En la fase de validación se llevó a cabo mediante jueces expertos en los que se consideró dos etapas: validación institucional y presentación Facultad de Ingeniería. En conclusión, un plan didáctico permitirá al estudiantado establecer una relación entre teoría y la práctica, posibilitando la construcción de creencias y percepciones respecto al futuro desempeño profesional.
Theory and practice of education, Education (General)
With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. In recent years, countries and regions such as China, the United States, and the European Union have increasingly recognized the importance of personalized learning, emphasizing its potential to integrate large-scale education with individualized instruction effectively. This survey provides a comprehensive analysis of personalized learning by reviewing relevant studies published in major conferences and journals between January 2017 and April 2025. We examine its definition, objectives, and underlying educational theories, highlighting its pedagogical significance. Furthermore, we explore personalized learning from two key dimensions: student modeling and personalized recommendations. Student modeling is analyzed from both cognitive and non-cognitive perspectives, while recommendation approaches are categorized based on their specific objectives. Additionally, we investigate the interplay between these components and their role in enhancing personalized learning. Beyond theoretical and algorithmic insights, this survey reviews real-world applications, demonstrating personalized learning's effectiveness in educational practice. Finally, we discuss key challenges and future directions, offering a multidimensional perspective that bridges theory and practice.
Edis Mekić, Mihailo Jovanović, Kristijan Kuk
et al.
This research paper will bring forth the innovative pedagogical approach in computer science education, which uses a combination of methodologies borrowed from Artificial Intelligence (AI) and DevOps to enhance the learning experience in Content Management Systems (CMS) Development. It has been done over three academic years, comparing the traditional way of teaching with the lately introduced AI-supported techniques. This had three structured sprints, each one of them covering the major parts of the sprint: object-oriented PHP, theme development, and plugin development. In each sprint, the student deals with part of the theoretical content and part of the practical task, using ChatGPT as an auxiliary tool. In that sprint, the model will provide solutions in code debugging and extensions of complex problems. The course includes practical examples like code replication with PHP, functionality expansion of the CMS, even development of custom plugins, and themes. The course practice includes versions' control with Git repositories. Efficiency will touch the theme and plugin output rates during development and mobile/web application development. Comparative analysis indicates that there is a marked increase in efficiency and shows effectiveness with the proposed AI- and DevOps-supported methodology. The study is very informative since education in computer science and its landscape change embodies an emerging technology that could have transformation impacts on amplifying the potential for scalable and adaptive learning approaches.
W trzech kolejnych artykułach ukazano przykłady uczniowskich adaptacji osobowości. Odwołując się do koncepcji analizy transakcyjnej oraz wiedzy z zakresu zaburzeń osobowości przedstawiono szereg typowych cech i zachowań, dominujących potrzeb oraz wybranych parametrów AT, a także podano praktyczne wskazówki mogące pomóc nauczycielom i wychowawcom w rozumieniu swoich relacji z uczniami sprawiającymi kłopoty wynikające z problemów charakterologicznych. Wskazano też sposoby działań kompensacyjnych oraz interwencji w postaci afirmacje służące zapobieganiu ujawniających się przejawów dysfunkcjonalnego rozwoju osobowości.
This paper explores the potential of artificial intelligence (AI) in higher education, specifically its capacity to replace or assist human teachers. By reviewing relevant literature and analysing survey data from students and teachers, the study provides a comprehensive perspective on the future role of educators in the face of advancing AI technologies. Findings suggest that although some believe AI may eventually replace teachers, the majority of participants argue that human teachers possess unique qualities, such as critical thinking, creativity, and emotions, which make them irreplaceable. The study also emphasizes the importance of social-emotional competencies developed through human interactions, which AI technologies cannot currently replicate. The research proposes that teachers can effectively integrate AI to enhance teaching and learning without viewing it as a replacement. To do so, teachers need to understand how AI can work well with teachers and students while avoiding potential pitfalls, develop AI literacy, and address practical issues such as data protection, ethics, and privacy. The study reveals that students value and respect human teachers, even as AI becomes more prevalent in education. The study also introduces a roadmap for students, teachers, and universities. This roadmap serves as a valuable guide for refining teaching skills, fostering personal connections, and designing curriculums that effectively balance the strengths of human educators with AI technologies. The future of education lies in the synergy between human teachers and AI. By understanding and refining their unique qualities, teachers, students, and universities can effectively navigate the integration of AI, ensuring a well-rounded and impactful learning experience.
This paper presents a critical analysis of generative Artificial Intelligence (AI) detection tools in higher education assessments. The rapid advancement and widespread adoption of generative AI, particularly in education, necessitates a reevaluation of traditional academic integrity mechanisms. We explore the effectiveness, vulnerabilities, and ethical implications of AI detection tools in the context of preserving academic integrity. Our study synthesises insights from various case studies, newspaper articles, and student testimonies to scrutinise the practical and philosophical challenges associated with AI detection. We argue that the reliance on detection mechanisms is misaligned with the educational landscape, where AI plays an increasingly widespread role. This paper advocates for a strategic shift towards robust assessment methods and educational policies that embrace generative AI usage while ensuring academic integrity and authenticity in assessments.
Purpose – The purpose of this paper is to explore perspectives of secondary (9–12) teachers on how to sustain growth mindset concepts within instructional practices as well as identifying barriers to sustainment. Design/methodology/approach – This study employed an exploratory case study to obtain the lived experiences of participants. An inductive analysis process was utilized on the data collected through structured interviews and a semi-structured focus group. Findings – Four major themes emerged from an in-depth analysis process: embed growth mindset practices in daily classroom instruction, communicate verbal affirmations and implement growth mindset learning tasks, allow students to experience academic successes and failures and teachers should receive continual support. Research limitations/implications – The study was limited to secondary grades (9–12). Therefore, it is recommended to expand the study to grades K-8. Originality/value – Due to a gap in the literature, this study provided insights into sustaining an innovative psychological approach, growth mindset, within academic instruction. Growth mindset concepts have been supported through the work of seminal researcher Carol Dweck and other prevalent educational researchers (e.g. Robert Marzano) to provide teachers with effective classroom instructional practices that can academically progress students.
For the exploration of the phase diagram of QCD, effective Polyakov loop theories derived from lattice QCD provide a valuable tool in the heavy quark mass regime. In practice, the evaluation of these theories is complicated by the appearance of long-range and multipoint interaction terms. On the other hand, it is well known that for theories with such kind of interactions mean field approximations can be expected to yield reliable results. Here, we apply this framework to the critical endpoint of the deconfinement transition and results are compared to the literature. This treatment can also be used to investigate the phase diagram at non-zero baryon and isospin chemical potential.
A cohomology theory of weighted Rota-Baxter $3$-Lie algebras is introduced. Formal deformations, abelian extensions, skeletal weighted Rota-Baxter $3$-Lie 2-algebras and crossed modules of weighted Rota-Baxter 3-Lie algebras are interpreted by using lower degree cohomology groups.
Maja van der Velden, Børge Kile Gjelsten, Siri Moe Jensen
Digitalisation creates opportunities and challenges, both socially and environmentally. Are computer science students interested in addressing these opportunities and challenges and is their education providing them the desired competencies? Theoretically, the paper focuses on the concept of competency and presents the eight sustainability competencies formulated in UNESCO’s Education for the Sustainable Development Goals. Two sets of data are analysed to address the research questions: data from a questionnaire focusing on their aspirations for future work and data from a deductive content analysis of the learning outcomes of six bachelor programmes in informatics. Sustainability-related factors scored important/very important in the aspirations for future work. The analysis of the learning outcomes indicates a very weak connection between learning goals and sustainability competencies. An integrative approach is proposed, which may contribute to the development of sustainability competencies that enable the students to take up normative and critical positions in digitalisation and sustainability discourses.
Jeffrey P. Carpenter, Torrey Trust, Royce Kimmons
et al.
Researchers have documented an array of ways Twitter hashtags offer digital spaces where educators can connect around interests and needs. During the early days of the COVID-19 pandemic, educators tweeted using various pandemic-related Twitter hashtags. In this study, we analyze data from two such hashtags: #remoteteaching and #remotelearning. We first data mined more than 36,000 tweets and then analyzed a random sample of 1,148 tweets and the accounts which sent those tweets. Our results suggest that the hashtags functioned as spaces in which a variety of education stakeholders engaged in activities that included knowledge sharing, social sharing, and information broadcasting. Alongside and sometimes entangled with such sharing, there was also a great deal of self-promotion. We discuss how these spaces appeared to offer potential benefits to educators navigating the transition to remote teaching but also consider how the presence of self-promotion may suggest downsides to such social mediums. We conclude with implications of these findings for education stakeholders and future research.
Electronic computers. Computer science, Theory and practice of education
This research is motivated by the lack of teachers' ability to prepare learning that develops high-level thinking skills (HOTS) of students. Therefore, this research aims to improve the ability of teachers to prepare lesson plans based on HOTS. This study used an action research design conducted at SMPN 2 Cerenti with 21 research subjects. Data were analyzed using observation sheets to assess the ability of teachers in preparing lesson plans based on HOTS. The results of the study showed that the ability of teachers in the first cycle reached 60.2%, which was categorized as enough, and in the second cycle, it reached 78.6%, which was categorized as good. Based on the results of the study, it can be concluded that the ability of teachers at SMPN 2 Cerenti in preparing lesson plans based on HOTS has increased through group guidance.