Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and policymakers to develop safe, effective, and equitable digital health interventions.
Debugging is a central yet complex activity in software engineering. Prior studies have documented debugging strategies and tool usage, but little theory explains how experienced developers reason about bugs in large, real-world codebases. We conducted a qualitative study using a grounded theory approach. We observed seven professional developers and five professional live-coding streamers working on 17 debugging tasks in their own codebases, capturing diverse contexts of debugging. We theorize debugging as a structured, iterative diagnostic process in which programmers update a mental model of the system to guide information gathering. Developers gather information by alternating between navigation and execution strategies, employing forward and backward tracing modes of reasoning and adapting these approaches according to codebase context, complexity, and familiarity. Developers also gather external resources to complement code-based evidence, with their experience enabling them to systematically construct a mental model. We contribute a grounded theory of professional debugging that surfaces the human-centered dimensions of the practice, with implications for tool design and software engineering education.
Stagediscriminatie is een hardnekkig verschijnsel dat ook in het hoger onderwijs een rol speelt. In een onderzoek bij vier opleidingen van een Randstedelijke hogeschool zijn de ervaringen van docenten en studenten rondom stagediscriminatie onderzocht. De resultaten tonen aan dat stagediscriminatie een structureel karakter heeft. Vooral studenten met een migratieachtergrond krijgen te maken met stagediscriminatie, zowel tijdens het sollicitatieproces als gedurende de stage zelf. Dit kan expliciet zijn, zoals het afwijzen van moslimstudenten vanwege een hoofddoek, of impliciet, via alledaags racisme zoals stereotyperende opmerkingen en micro-agressie. Internationale studenten ondervinden uitsluiting vanwege institutionele belemmeringen die afbreuk doen aan hun gelijke kansen. Stagediscriminatie manifesteert zich vaak subtiel, waardoor studenten moeite hebben om het expliciet te benoemen. Dit heeft negatieve gevolgen voor hun zelfvertrouwen en mentale gezondheid. Docenten geven aan zich handelingsverlegen te voelen als studenten hen benaderen over stagediscriminatie, wat ertoe leidt dat veel studenten zich onvoldoende gesteund voelen door hun opleiding. Dit gebrek aan bewustwording en ondersteuning maakt het moeilijk voor studenten om discriminatie te herkennen en er actie tegen te ondernemen. Het onderzoek onderstreept de urgentie van het bespreekbaar maken van stagediscriminatie door opleidingen, het vergroten van de bewustwording en het trainen en voorlichten van zowel docenten als studenten.
Developing expert-like problem-solving skills is a central goal of undergraduate physics education. In this study, we investigate the impact of teaching explicit problem-solving frameworks, combined with deliberate practice, on students' problem-solving approaches. Using multidimensional scaling to analyze students' decision-making patterns, we compare the similarity of students taught with these methods to physics experts and to students taught with traditional repeated practice. Our results show that students who received structured frameworks and targeted feedback through deliberate practice exhibited problem-solving behaviors significantly more aligned with those of experts. These findings suggest that pedagogies emphasizing explicit strategy instruction with feedback are more effective than rote repetition for fostering expertise. We recommend integration of these approaches into physics curricula to better support the development of skilled and adaptive problem solvers.
This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system. The scarcity of real interview data, absence of structured variable modeling, and lack of interpretability in inference mechanisms have limited the analytical accuracy and policy responsiveness of EII research. To resolve these challenges, we propose a structural modeling paradigm based on the National Institute of Standards and Technology (NIST) synthetic data quality framework, focusing on consistency, authenticity, and traceability. We design a five-layer architecture that includes prompt-driven synthetic dialogue generation, a structured variable system covering skills, institutional, and emotional dimensions, dependency and causal path modeling, graph-based structure design, and an interactive inference engine. Empirical results demonstrate the effectiveness of the approach using a 15-segment synthetic corpus, with 41,597 tokens, 127 annotated variables, and 820 semantic relationship triples. The model exhibits strong structural consistency (Krippendorff alpha = 0.83), construct validity (RMSEA = 0.048, CFI = 0.93), and semantic alignment (mean cosine similarity > 0.78 via BERT). A key causal loop is identified: system mismatch leads to emotional frustration, reduced participation, skill gaps, and recurrence of mismatch, revealing a structural degradation cycle. This research introduces the first NIST-compliant AI modeling framework for stakeholder systems and provides a foundation for policy simulation, curriculum design, and collaborative strategy modeling.
Given the increasing demands in computer programming education and the rapid advancement of large language models (LLMs), LLMs play a critical role in programming education. This study provides a systematic review of selected empirical studies on LLMs in computer programming education, published from 2023 to March 2024. The data for this review were collected from Web of Science (SCI/SSCI), SCOPUS, and EBSCOhost databases, as well as three conference proceedings specialized in computer programming education. In total, 42 studies met the selection criteria and were reviewed using methods, including bibliometric analysis, thematic analysis, and structural topic modeling. This study offers an overview of the current state of LLMs in computer programming education research. It outlines LLMs' applications, benefits, limitations, concerns, and implications for future research and practices, establishing connections between LLMs and their practical use in computer programming education. This review also provides examples and valuable insights for instructional designers, instructors, and learners. Additionally, a conceptual framework is proposed to guide education practitioners in integrating LLMs into computer programming education. This study suggests future research directions from various perspectives, emphasizing the need to expand research methods and topics in computer programming education as LLMs evolve. Additionally, future research in the field should incorporate collaborative, interdisciplinary, and transdisciplinary efforts on a large scale, focusing on longitudinal research and development initiatives.
Welington Fabrício dos Santos Costa, Jesuíno Alves Martins Júnior, André Flávio Gonçalves Silva
et al.
In recent years, the frequency of extreme weather events on Earth has increased significantly. This phenomenon is driven by the intensification of the greenhouse effect caused by anthropogenic activities, leading to temperature variations in urban environments that affect thermal comfort and quality of life. Given this context, the present study investigates temperature mapping in urban biomes using an infrared thermometer, conducted as part of a hands-on workshop offered during the 21st National Science and Technology Week. The initiative involved students from the public school system and was grounded in Physics education, aiming to foster scientific enculturation. Participants engaged in a problem-based learning experience, actively contributing to all stages of the knowledge construction process. The objective was to examine the relationship between vegetation presence and its impact on temperature in urban environments. A qualitative and quantitative methodological approach was adopted, enabling the identification of scientific literacy indicators such as information sequencing, data organization, logical reasoning, hypothesis formulation, justification, and explanation of observed phenomena. The analysis of students' statements and activity guidelines provided insights into their critical thinking development. The findings indicate that students developed essential skills for understanding physical and environmental phenomena, effectively linking collected data to scientific concepts and proposing well-supported interpretations. Moreover, the experience reinforced the perception of science as a dynamic and investigative process, fostering curiosity and enhancing students' argumentative abilities. Thus, the workshop proved to be an effective strategy for promoting scientific literacy and engaging participants in the study of environmental impacts in urban contexts.
Alla Durdas, Nataliia Furmanchuk, Anna Bondar
et al.
This article focuses on the importance of developing speaking skills at foreign language classes at university, as the ability to communicate in English is one of the essential requirements for success in various careers both nationally and internationally. However, while speaking a foreign language university students face challenges like hesitation, limited vocabulary, anxiety and lack of real-world practice. Traditional methods often struggle to address these issues due to large classes, limited interaction and time constraints. Teaching speaking is particularly demanding, requiring focus on pronunciation, grammar and fluency, along with overcoming shyness, low confidence and fear of judgment. To overcome these barriers, teachers should use innovative methods and technologies. Techniques like communicative language teaching, task-based learning, immersion, and digital tools help students build confidence and fluency. Activities like role-playing and digital storytelling make language use more natural and reduce anxiety. The article highlights the importance of adapting teaching methods to students’ needs, focusing on real-world application of a foreign language like professional communication. Practical strategies include regular practice, engaging with native content, cultural exploration and learning from mistakes. By creating supportive and interactive environments, teachers can help students to become confident speakers, preparing them for successful work and realization in a globalized world.
Tony Haoran Feng, Andrew Luxton-Reilly, Burkhard C. Wünsche
et al.
Generative Artificial Intelligence (GenAI) offers numerous opportunities to revolutionise teaching and learning in Computing Education (CE). However, educators have expressed concerns that students may over-rely on GenAI and use these tools to generate solutions without engaging in the learning process. While substantial research has explored GenAI use in CE, and many Computer Science (CS) educators have expressed their opinions and suggestions on the subject, there remains little consensus on implementing curricula and assessment changes. In this paper, we describe our experiences with using GenAI in CS-focused educational settings and the changes we have implemented accordingly in our teaching in recent years since the popularisation of GenAI. From our experiences, we propose two primary actions for the CE community: 1) redesign take-home assignments to incorporate GenAI use and assess students on their process of using GenAI to solve a task rather than simply on the final product; 2) redefine the role of educators to emphasise metacognitive aspects of learning, such as critical thinking and self-evaluation. This paper presents and discusses these stances and outlines several practical methods to implement these strategies in CS classrooms. Then, we advocate for more research addressing the concrete impacts of GenAI on CE, especially those evaluating the validity and effectiveness of new teaching practices.
Understanding teachers' perspectives on AI in Education (AIEd) is crucial for its effective integration into the educational framework. This paper aims to explore how teachers currently use AI and how it can enhance the educational process. We conducted a cross-national study spanning Greece, Hungary, Latvia, Ireland, and Armenia, surveying 1754 educators through an online questionnaire, addressing three research questions. Our first research question examines educators' understanding of AIEd, their skepticism, and its integration within schools. Most educators report a solid understanding of AI and acknowledge its potential risks. AIEd is primarily used for educator support and engaging students. However, concerns exist about AI's impact on fostering critical thinking and exposing students to biased data. The second research question investigates student engagement with AI tools from educators' perspectives. Teachers indicate that students use AI mainly to manage their academic workload, while outside school, AI tools are primarily used for entertainment. The third research question addresses future implications of AI in education. Educators are optimistic about AI's potential to enhance educational processes, particularly through personalized learning experiences. Nonetheless, they express significant concerns about AI's impact on cultivating critical thinking and ethical issues related to potential misuse. There is a strong emphasis on the need for professional development through training seminars, workshops, and online courses to integrate AI effectively into teaching practices. Overall, the findings highlight a cautious optimism among educators regarding AI in education, alongside a clear demand for targeted professional development to address concerns and enhance skills in using AI tools.
Kristin A. Oliver, Victoria Borish, Bethany R. Wilcox
et al.
As quantum technologies transition out of the research lab and into commercial applications, it becomes important to better prepare students to enter this new and evolving workforce. To work towards this goal of preparing physics students for a career in the quantum industry, a senior capstone course called "Quantum Forge" was created at the University of Colorado Boulder. This course aims to provide students a hands-on quantum experience and prepare them to enter the quantum workforce directly after their undergraduate studies. Some of the course's goals are to have students understand what comprises the quantum industry and have them feel confident they could enter the industry if desired. To understand to what extent these goals are achieved, we followed the first cohort of Quantum Forge students through their year in the course in order to understand their perceptions of the quantum industry including what it is, whether they feel that they could be successful in it, and whether or not they want to participate in it. The results of this work can assist educators in optimizing the design of future quantum-industry-focused courses and programs to better prepare students to be a part of this burgeoning industry.
This paper explores the causal impact of education opportunities on rural areas by exploiting the higher education expansion (HEE) in China in 1999. By utilizing the detailed census data, the cohort-based difference-in-differences design indicates that the HEE increased college attendance and encouraged more people to attend senior high schools and that the effect is more significant in rural areas. Then we apply a similar approach to a novel panel data set of rural villages and households to examine the effect of education opportunities on rural areas. We find contrasting impacts on income and life quality between villages and households. Villages in provinces with higher HEE magnitudes underwent a drop in the average income and worse living facilities. On the contrary, households sending out migrants after the HEE experienced an increase in their per capita income. The phenomenon where villages experienced a ``brain drain'' and households with migrants gained after the HEE is explained by the fact that education could serve as a way to overcome the barrier of rural-urban migration. Our findings highlight the opposed impacts of education opportunities on rural development and household welfare in rural areas.
Giulio Barbero, Marcello M. Bonsangue, Felienne F. J. Hermans
Adding game elements to higher education is an increasingly common practice. As a result, many recent empirical studies focus on studying the effectiveness of gamified or game-based educational experiences. The findings of these studies are very diverse, showing both positive and negative effects, and thus calling for comparative meta-studies. In this paper we review and analyze different studies, aiming to summarise and evaluate controlled experiments conducted within different scientific disciplines. We focus on the clarity of non-experimental conditions' descriptions and show that in most cases a. educational methods used in control groups' activities are poorly described, b. educational materials used in control groups' activities are often unclear, and c. the starting conditions are unclear. We also noticed that studies in the fields of computer science and engineering, in general, report results more clearly than in other fields. Based on the above finding, we conclude with a few recommendations for the execution of future empirical studies of games in education for the sake of allowing a more structured comparison.
The aim of study is to develop a technology for effective interaction between universities and business based on the analysis of the problems of vocational education in the era of digital transformation. The article examines the changes that have occurred in the business processes of organizations and highlights the key gaps between the level of graduate qualifications and employer requirements. To solve the identified problems, it is proposed to establish a systemic collaboration between business, science and education. The following forms of interaction between business and universities are being explored: case study technologies, educational hackathons and scientific crowdsourcing. These technologies can bring mutual benefits to all stakeholders. For the interaction of the educational and scientific components of the university, a new concept of student association called student scientific enterprise is proposed. This association is built on the principles of self-organization and self-government and has the characteristics of a real enterprise. Its main goal is to involve students in scientific work and adapt young people to future professional activities. Scientific novelty: a theoretical model of collaboration between business and universities for mutual benefit has been developed. As a result of the study, forms of interaction among science, professional education and business were proposed.
Uncertainty is an important and fundamental concept in physics education. Students are often first exposed to uncertainty in introductory labs, expand their knowledge across lab courses, and then are introduced to quantum mechanical uncertainty in upper-division courses. This study is part of a larger project evaluating student thinking about uncertainty across these contexts. In this research, we investigate advanced physics student thinking about uncertainty by asking them conceptual questions about how a hypothetical distribution of measurements would change if `more' or `better' data were collected in four different experimental scenarios. The scenarios include both classical and quantum experiments, as well as experiments that theoretically result in an expected single value or an expected distribution. This investigation is motivated by our goal of finding insights into students' potential point- and set-like thinking about uncertainty and of shining light on the limitations of those binary paradigms.
Shabir Ahmad, Sabina Umirzakova, Ghulam Mujtaba
et al.
We are currently in a post-pandemic era in which life has shifted to a digital world. This has affected many aspects of life, including education and learning. Education 5.0 refers to the fifth industrial revolution in education by leveraging digital technologies to eliminate barriers to learning, enhance learning methods, and promote overall well-being. The concept of Education 5.0 represents a new paradigm in the field of education, one that is focused on creating a learner-centric environment that leverages the latest technologies and teaching methods. This paper explores the key requirements of Education 5.0 and the enabling technologies that make it possible, including artificial intelligence, blockchain, and virtual and augmented reality. We analyze the potential impact of these technologies on the future of education, including their ability to improve personalization, increase engagement, and provide greater access to education. Additionally, we examine the challenges and ethical considerations associated with Education 5.0 and propose strategies for addressing these issues. Finally, we offer insights into future directions for the development of Education 5.0, including the need for ongoing research, collaboration, and innovation in the field. Overall, this paper provides a comprehensive overview of Education 5.0, its requirements, enabling technologies, and future directions, and highlights the potential of this new paradigm to transform education and improve learning outcomes for students.
Jaromir Savelka, Arav Agarwal, Christopher Bogart
et al.
We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level. Discussions of potential uses (e.g., exercise generation, code explanation) and misuses (e.g., cheating) of this emerging technology in programming education have intensified, but to date there has not been a rigorous analysis of the models' capabilities in the realistic context of a full-fledged programming course with diverse set of assessment instruments. We evaluated GPT on three Python courses that employ assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Further, we studied if and how successfully GPT models leverage feedback provided by an auto-grader. We found that the current models are not capable of passing the full spectrum of assessments typically involved in a Python programming course (<70% on even entry-level modules). Yet, it is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score (>55%) in introductory and intermediate courses alike. While the models exhibit remarkable capabilities, including correcting solutions based on auto-grader's feedback, some limitations exist (e.g., poor handling of exercises requiring complex chains of reasoning steps). These findings can be leveraged by instructors wishing to adapt their assessments so that GPT becomes a valuable assistant for a learner as opposed to an end-to-end solution.
The paper aims to substantiate the need to define primary school teachers’ professional competence in order to improve the quality of chess education. The paper analyses various definitions of professional competence and its components, the particularities of chess education implementation in primary schools, the requirements for teachers, the experience of research and methodological support of teachers of universal chess education. Scientific novelty lies in shedding light on the content of the concept “professional competence of primary school teachers implementing chess education programmes” and identifying the component composition of the professional competence of primary school teachers implementing chess education programmes. As a result, the author has defined the professional competence of primary school teachers implementing chess education programmes and has identified its constituent components.
One of the biggest criticisms of the Set Shaping Theory is the lack of a practical application. This is due to the difficulty of its application. In fact, to apply this technique from an experimental point of view we must use a table that defines the correspondences between two sets. However, this approach is not usable in practice, because the table has A^N elements, with A number of symbols and N length of the message to be encoded. Consequently, these tables can be implemented in a program only when A and N have a low value. Unfortunately, in these cases, there are no compression algorithms with such efficiency as to detect the improvement introduced by this method. In this article, we use a function capable of performing the transform without using the correspondence table; this allows us to apply this theory to a wide range of values of A and N. The results obtained confirm the theoretical predictions.