Search Results (40)

This research highlights the importance of Emotion Aware Technologies (EAT) and their implementation in serious games to assist children with Autism Spectrum Disorder (ASD) in developing social-emotional skills. As AI is gaining popularity, such tools can be used in mobile applications as invaluable teaching tools. In this paper, a new AI framework application is discussed that will help children with ASD develop efficient social-emotional skills. It uses the Jetpack Compose framework and Google Cloud Vision API as emotion-aware technology. The framework is developed with two main features designed to help children reflect on their emotions, internalise them, and train them how to express these emotions. Each activity is based on similar features from literature with enhanced functionalities. A diary feature allows children to take pictures of themselves, and the application categorises their facial expressions, saving the picture in the appropriate space. The three-level minigame consists of a series of prompts depicting a specific emotion that children have to match. The results of the framework offer a good starting point for similar applications to be developed further, especially by training custom models to be used with ML Kit. Full article

This study explores how artificial intelligence (AI) technologies can be theoretically integrated into literature curriculum development, using the works of Anton Chekhov and Lu Xun as illustrative case texts. The aim is to reduce barriers to language and cultural understanding in literature education and increase the efficiency and accessibility of cross-cultural teaching. We used natural language processing (NLP) techniques to analyze textual features, such as readability index, lexical density, and syntactic complexity, of AI-generated and human-translated “The Chameleon” and “A Madman’s Diary”. Teaching cases from universities in China, Russia, and Kazakhstan are reviewed to assess the emerging practice of AI-supported literature teaching. The proposed theoretical framework draws on hermeneutics, posthumanism, and cognitive load theories. The results of the data-driven analysis suggest that AI-assisted translation tends to simplify sentence structure and improve surface readability. While anecdotal classroom observations highlight the role of AI in initial comprehension, deeper literary interpretation still relies on teacher guidance and critical human engagement. This study introduces a conceptual “AI Literature Teaching Model” that positions AI as a cognitive and cultural mediator and outlines directions for future empirical validation. Full article

To correspond to the advancement of technology, programming has become an indispensable course in university curricula. However, students easily become confused by the rules governing program execution or by complex logical structures. Mastering program structure and logic often is a significant challenge for beginners, especially. Despite the availability of information on programming on various websites and tools, including generative artificial intelligence (AI), there is still a gap between conceptual understanding and practical application for beginners. They overlook important implementation details or struggle to grasp the flow of a program, making the mastery of program logic a persistent challenge. To address these issues, we have developed a system that dynamically generates process architecture diagrams. Users upload their code, and the system produces corresponding diagrams that decompose and execute the code line by line. Its visual representation allows users to observe the program’s execution and aids them in comprehending the sequence and operational flow of the code. By understanding the structure and logic of the program intuitively, this system supplements traditional teaching methods and AI-assisted question-and-answer tools. The experimental results demonstrated that students found the system helpful to track their learning progress (87%) and improved their understanding of program code (81%). Additionally, 84% of students reported that the system was easy to use, highlighting its user-friendliness. In terms of student interest, 83% of students agreed that the interactive elements made learning more engaging, indicating that the system positively contributed to dynamic and enjoyable learning. However, 63% of students reported an improvement in coding and were influenced by the complexity of the programming tasks assigned. Despite this, the overall satisfaction with the system developed in this study was high. Full article

In Industry 5.0 it is essential that humans are in the loop of technology integration of industry processes. With the advancements of Generative Artificial Intelligence (GenAI), a lot of new opportunities and challenges for learning and teaching are present. Many students already use publicly available AI Digital Assistants (p-AIDA) like ChatGPT for academic purposes. However, there are concerns around the use of such p-AIDA tools, particularly in terms of academic integrity, data privacy, intellectual property, and the impact on the quality of education. Furthermore, many higher education institutions have substantial learning materials and data about students that they may not want to share with p-AIDA. Therefore, using the Technology Acceptance Model (TAM) and following a Design-Based Research (DBR) approach, we explored the perspectives and experiences of a beta-test of an institutionally developed AIDA (i-AIDA) with 18 UK students using multiple methods and data sources (including pre-post-test, interviews, think-aloud, and prompt analysis). Our research underscores the potential benefits and limitations of in-house i-AIDA in enhancing learning experiences without compromising academic integrity or privacy, and how higher education institutions can prepare themselves for Industry 5.0. Full article

Generative AI tools offer opportunities for enhancing learning and assessment, but raise concerns about equity, academic integrity, and the ability to critically engage with AI-generated content. This study explores these issues within a psychology-oriented postgraduate programme at a UK university. We co-designed and evaluated a novel AI-integrated assessment aimed at improving critical AI literacy among students and teaching staff (pre-registration: osf.io/jqpce). Students were randomly allocated to two groups: the ‘compliant’ group used AI tools to assist with writing a blog and critically reflected on the outputs, while the ‘unrestricted’ group had free rein to use AI to produce the assessment. Teaching staff, blinded to group allocation, marked the blogs using an adapted rubric. Focus groups, interviews, and workshops were conducted to assess the feasibility, acceptability, and perceived integrity of the approach. Findings suggest that, when carefully scaffolded, integrating AI into assessments can promote both technical fluency and ethical reflection. A key contribution of this study is its participatory co-design and evaluation method, which was effective and transferable, and is presented as a practical toolkit for educators. This approach supports growing calls for authentic assessment that mirrors real-world tasks, while highlighting the ongoing need to balance academic integrity with skill development. Full article

Bridging the gender gap in STEM remains a critical challenge, with nearly 70% of the STEM workforce being male. Prior research suggests that integrating educational robotics (ER) into the STEM curricula can boost young girls’ motivation before gender stereotypes and societal norms discourage their pursuit of STEM-related academic pursuits and career paths. The success of these activities depends on inclusive topics, materials, and teaching approaches, as a lack of diversity in these elements may lead to disengagement among young girls. To address this, educators must develop gender-neutral ER curricula and activities. However, due to the persistence of unconscious bias and gender stereotypes, determining whether an activity is truly gender-neutral can be difficult. This study explores the potential of ChatGPT 3.5, a widely used Artificial Intelligence (AI) tool, to assist educators in designing gender-neutral ER activities. Specifically, we investigate whether ChatGPT can (i) generate gender-neutral activities that serve as a foundation for educators, and (ii) identify unconscious bias, gender stereotypes, or demotivating elements (e.g., activity topics, materials) in suggested ER activities. To ensure consistency and depth in our analysis, we performed several repetitions of each prompt, examining the variations and commonalities across outputs. Our results indicate that AI tools like ChatGPT can both highlight biases in existing activities and assist in the development of more inclusive, unbiased learning experiences. Full article

Teaching social research methodology in university courses, whether qualitative or quantitative, presents significant challenges for both instructors and students. These challenges include the availability of empirical datasets, the illustration of data analysis techniques, the simulation of research report writing, and the facilitation of scenario-based learning. Emerging AI tools, such as ChatGPT-4, offer potential support in higher education, though their effectiveness depends on the context and their integration with traditional didactic methods. This article explores the potential of AI in teaching social research methodology, with a focus on its benefits, limits and ethical considerations. Furthermore, the paper presents a case study of AI application in teaching qualitative research techniques, specifically in the analysis of solicited documents. Generative AI shows the potential to improve the teaching of social research methodology by providing students with opportunities to engage in hands-on learning, interact with realistic datasets and refine their analytical and communication skills. The integration of AI in education should, however, be approached with a critical mindset, ensuring that AI tools serve as a means to sharpen (not replace) traditional methods of learning. Full article

Despite the global integration of generative artificial intelligence (GenAI) tools in higher education, limited research exists on how demographic factors such as gender and academic level shape their adoption and usage, particularly in language learning contexts outside Western settings. This study aimed to fill this gap by examining the usage patterns, satisfaction levels, and acceptance factors of GenAI tools among English major students in Indonesian higher education. Employing a mixed-methods approach, the research collected data from 277 students using surveys and structured interviews to gauge both quantitative and qualitative aspects of GenAI tool utilization. The results indicate ChatGPT, Google Translate, and Grammarly as the most utilized tools for writing assistance, language learning, and research tasks, with consistent satisfaction across demographics. Performance expectancy emerged as the most influential acceptance factor, followed by effort expectancy and facilitating conditions, while social influence played a moderate role. Qualitative findings reveal students rely on GenAI for grammar refinement, translation accuracy, content exploration, and idea generation, reflecting critical and reflective engagement. Nonetheless, concerns about overreliance and ethical implications accentuate the need for balanced integration. These findings inform tailored educational strategies, emphasizing ethical use and fostering critical thinking in GenAI adoption for English language education. Full article

Background/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of metastatic disease. The lymph nodes and omentum are almost universally included in such resection cases and contribute considerably to this burden, principally due to volume rather than task complexity. To date, artificial intelligence (AI)-based studies have reported good success rates in identifying nodal spread in other malignancies, but the development of such time-saving assistive digital solutions has been neglected in ovarian cancer. This study aimed to detect the presence or absence of metastatic ovarian carcinoma in the lymph nodes and omentum. Methods: We used attention-based multiple-instance learning (ABMIL) with a vision-transformer foundation model to classify whole-slide images (WSIs) as either containing ovarian carcinoma metastases or not. Training and validation were conducted with a total of 855 WSIs of surgical resection specimens collected from 404 patients at Leeds Teaching Hospitals NHS Trust. Results: Ensembled classification from hold-out testing reached an AUROC of 0.998 (0.985–1.0) and a balanced accuracy of 100% (100.0–100.0%) in the lymph node set, and an AUROC of 0.963 (0.911–0.999) and a balanced accuracy of 98.0% (94.8–100.0%) in the omentum set. Conclusions: This model shows great potential in the identification of ovarian carcinoma nodal and omental metastases, and could provide clinical utility through its ability to pre-screen WSIs prior to histopathologist review. In turn, this could offer significant time-saving benefits and streamline clinical diagnostic workflows, helping to address the chronic staffing shortages in histopathology. Full article

Generative artificial intelligence tools, such as Microsoft Copilot, are transforming the teaching of programming by providing real-time feedback and personalized assistance; however, their impact on learning, motivation, and cognitive absorption remains underexplored, particularly in university settings. This study evaluates the effectiveness of Microsoft Copilot compared to instructional videos in teaching web programming in PHP, implementing a quasi-experimental design with 71 industrial engineering students in Chile, divided into two groups: one using Microsoft Copilot and the other following instructional videos, with pre- and post-tests applied to measure knowledge acquisition while surveys based on the Hedonic-Motivation System Adoption Model (HMSAM) assessed cognitive absorption (enjoyment, control, immersion, curiosity) and technology acceptance (perceived usefulness, ease of use, and intention to adopt). The results show that, while both methods improved learning, students who used instructional videos achieved greater knowledge gains, higher levels of curiosity, and a stronger intention to continue using the technique, suggesting that instructional videos, by providing structured explanations and reducing cognitive load, may be more effective in the early stages of programming learning. In contrast, AI tools could be more beneficial in advanced stages where students require adaptive feedback, providing empirical evidence on the comparative effectiveness of AI-based and video-based instruction in teaching programming and highlighting the importance of balancing structured learning with AI-driven interactivity, with the recommendation that educators integrate both approaches to optimize the learning experience, using videos for initial instruction and AI tools for personalized support. Full article

The exceptional progress in artificial intelligence is transforming the landscape of technical jobs and the educational requirements needed for these. This study’s purpose is to present and evaluate an intuitive open-source framework that transforms existing courses into interactive, AI-enhanced learning environments. Our team performed a study on the proposed method’s advantages in a pilot population of teachers and students which assessed it as “involving, trustworthy and easy to use”. Furthermore, we evaluated the AI components on standard large language model (LLM) benchmarks. This free, open-source, AI-enhanced educational platform can be used to improve the learning experience in all existing secondary and higher education institutions, with the potential of reaching the majority of the world’s students. Full article

In light of the remarkable success of large language models (LLMs) in natural language understanding and generation, a trend of applying LLMs to professional domains with specialized requirements stimulates interest across various fields. It is desirable to further understand the level of intelligence that can be achieved by LLMs in solving domain-specific problems, as well as the resources that need to be invested accordingly. This paper studies the problem of generating high-quality test questions with specified knowledge points and target cognitive levels in AI-assisted teaching and learning. Our study shows that LLMs, even those as immense as GPT-4 or Bard, can hardly fulfill the design objectives, lacking clear focus on cognitive levels pertaining to specific knowledge points. In this paper, we explore the opportunity of enhancing the capability of LLMs through system design, instead of training models with substantial domain-specific data, consuming mass computing and memory resources. We propose a novel design scheme that orchestrates a dual-LLM engine, consisting of a question generation model and a cognitive-level evaluation model, built with fine-tuned, lightweight baseline models and prompting technology to generate high-quality test questions. The experimental results show that the proposed design framework, TwinStar, outperforms the state-of-the-art LLMs for effective test question generation in terms of cognitive-level adherence and knowledge relevance. TwinStar implemented with ChatGLM2-6B improves the cognitive-level adherence by almost 50% compared to Bard and 21% compared to GPT-4.0. The overall improvement in the quality of test questions generated by TwinStar reaches 12.0% compared to Bard and 2% compared with GPT-4.0 while our TwinStar implementation consumes only negligible memory space compared with that of GPT-4.0. An implementation of TwinStar using LLaMA2-13B shows a similar trend of improvement. Full article

In recent years, the educational field has evolved rapidly owing to the integration of several technologies, especially experiments in remote laboratories in the engineering area. Therefore, this article addresses the development of an innovation system for automatically correcting experiments in remote laboratories in mechatronics using digital twins, convolutional neural networks (CNNs), and generative artificial intelligence technologies. This system was designed to overcome the limitations of physical laboratories and teacher’s availability and assist in learning, enabling automatic acquisitions at any time. The digital twin captures data from the teacher’s and student’s experiments, allowing accurate comparisons to identify successes and errors. The application of CNNs serves to validate the results of the experiments through image analysis, whereas generative AI helps to identify patterns. The system was evaluated in a didactic plant, effectively correcting experiments with digital inputs and outputs. In addition, it provides students with detailed feedback on their performance, including specific errors and suggestions for improvement. With a three-layer architecture, i.e., experiments, didactics, and management, the system efficiently processes data from teachers and students, contributing to correcting experiments and optimizing teaching in remote environments. Full article

The advancement of artificial intelligence (AI) has attracted attention across disciplines. Different research has revealed the role of AI and the outcomes of AI in education (AIEd). However, teachers need to use AI to cater to learner diversities in mathematics education, which needs exploration. Therefore, how different AI tools assist mathematics teachers in developing teaching materials for students was investigated in this study. Teachers were invited to utilize AI techniques to develop their teaching and learning materials. The findings can be used to enhance the remedial and enrichment measures in teaching secondary mathematics and construct a framework to help teachers with learner diversities with AI tools. Full article

This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the scientific literature was conducted, analyzing 370 articles published between 2006 and 2024. The research examines how AI can support the identification of learning patterns and individual student needs. Through EDM, student data are analyzed to predict student performance and enable timely interventions. HITL-ML ensures that educators remain in control, allowing them to adjust the system according to their pedagogical goals and minimizing potential biases. Machine-assisted teaching allows AI processes to be structured around specific learning criteria, ensuring relevance to educational outcomes. The findings suggest that these AI applications can significantly improve personalized learning, student tracking, and resource optimization in educational institutions. The study highlights ethical considerations, such as the need to protect privacy, ensure the transparency of algorithms, and promote equity, to ensure inclusive and fair learning environments. Responsible implementation of these methods could significantly improve educational quality. Full article