Search Results (11)

Personalized learning in higher education aims to enhance student motivation, engagement, and academic outcomes. Learner personas as representations of students offer a promising approach to personalizing learning in technology-enhanced environments, particularly in combination with learning analytics (LA). This study explores how LA can be used to identify activity patterns based on data from the E-Learning Activities Recommender System (ELARS). The activity data of STEM students (N = 90) were analyzed using K-Means clustering. The analyses were based on timing, the percentage of task completion, and their combination to identify distinct engagement patterns. Based on these, six clusters (learner personas) were identified: consistent performers, overachievers, last-minute underperformers, low-engagement students, late moderate achievers, and early proactive performers. For each persona, GenAI prompts and personalized interventions based on motivational and instructional frameworks were proposed. These will inform further development of the ELARS system, with the goal of enabling personalization, promoting self-regulated learning, and encouraging students to integrate GenAI tools into their learning. The study shows how the combination of clustering techniques for learner persona development with GenAI prompt engineering and adaptive interventions has the potential to drive the design of personalized learning environments. Full article

This study investigates the integration of mentorship, self-regulated learning (SRL), and generative artificial intelligence (gen-AI) to support pre-service teachers (PSTs) before and during work-integrated learning (WIL) placements. Utilising the Mentoring and SRL Pyramid Model (MSPM), it examines how mentors’ dual roles as coaches and assessors influence PSTs’ SRL and explores to what extent gen-AI can assist PSTs in meeting the demands of WIL placements. Quantitative and qualitative data from 151 PSTs, including survey, interview, placement scores, and mentor feedback were analysed using statistical correlation analysis and thematic analysis to reveal varied mentorship approaches. Gen-AI tools are highlighted as valuable in enhancing PSTs’ SRL, providing tactical and emotional guidance where traditional mentorship is limited. However, challenges remain in gen-AI’s ability to navigate complex interpersonal dynamics. The study advocates for balanced mentorship training that integrates technical and emotional support, and equitable access to gen-AI tools. These insights are critical for educational institutions aiming to optimise PST experiences and outcomes in WIL through strategic integration of gen-AI and mentorship. Full article

This paper summarizes the findings and main conclusions from the first presentation of the module “CETD23: Cómo entrenar a tu dragón: la inteligencia artificial generativa como herramienta para mejorar el aprendizaje en entornos online e híbridos”. This is an optional module accredited through the ECTS (European Credit Transfer System) and delivered as part of the “Plan de Formación de Docencia y Personal Investigador 2021–2025” of the Universidad de Las Palmas de Gran Canaria (ULPGC). The Plan de Formación is a development programme offered by Spanish universities to new and existing teaching staff, aimed at improving the quality of their teaching practises in line with Aneca’s Docencia regulations (like the PGCERT and PGCAPT programmes in the UK). The aim of module CETD23 is to explore the use of Generative AI (GenAI) to enhance learning and teaching and to build the AI literacy of ULPGC’s teaching staff. The module received high student satisfaction, with an average score of 4.84 on the Likert Scale, and achieved a 100% completion rate for the final summative project. The final conclusions highlight the need for universities to establish reglamentos (policies and guidance) on how to use GenAI to enhance learning and assessment, as well as to involve students as equal partners in the design and assessment of methods that use AI. Full article

Autonomous vehicles (AVs) are expected to eventually replace traditional vehicles that require human drivers. In recent years, several AV manufacturers have begun on-road testing to validate the safety of these vehicles. California is one of the few states to permit such testing, regulating it through a permit system. To ensure transparency and public awareness, the state mandates that any licensed AV manufacturer conducting on-road tests report crashes involving AVs. This must be conducted using a standardized format known as OL316, a requirement that has been in place since late 2014. While previous research has explored AV crash data, most studies have focused on specific timeframes without covering the entire period since 2014. Moreover, converting the data from PDFs to machine-readable formats has often been a manual process, and the description text field in reports has rarely been fully analyzed. This article presents a comprehensive, machine-readable dataset of AV crashes from 2014 to September 2024, along with publicly available parsing code to streamline future data analysis. Additionally, we provide an updated statistical analysis of AV crashes during this period. Furthermore, we leverage Generative AI (GenAI) to analyze the description text field of the OL316 reports. This analysis identifies common crash scenarios, contributing factors, and additional insights into moderate and major incidents. The final dataset comprises 728 crash entries. Notably, only 2% of the crashes were categorized as major, while 14% were classified as moderate. Furthermore, 43% of the crashes occurred while the AV was stationary, whereas 55% took place while the AV was in motion. Our GenAI analysis indicates that, in many instances, human drivers of non-autonomous vehicles were at fault. Common causes include rear-end collisions due to insufficient following distances, traffic violations such as running red lights or stop signs, and reckless behaviors like lane boundary violations or speeding. Full article

Most of today’s educators are in no shortage of digital and online learning technologies available at their fingertips, ranging from Learning Management Systems such as Canvas, Blackboard, or Moodle, online meeting tools, online homework, and tutoring systems, exam proctoring platforms, computer simulations, and even virtual reality/augmented reality technologies. Furthermore, with the rapid development and wide availability of generative artificial intelligence (GenAI) services such as ChatGPT, we are just at the beginning of harnessing their potential to transform higher education. Yet, facing the large number of available options provided by cutting-edge technology, an imminent question on the mind of most educators is the following: how should I choose the technologies and integrate them into my teaching process so that they would best support student learning? We contemplate over these types of important and timely questions and share our reflections on evidence-based approaches to harnessing digital learning tools using a Self-regulated Engaged Learning Framework we have employed in our research in physics education that can be valuable for educators in other disciplines. Full article

The rapid advancements of Generative Artificial Intelligence (GenAI) technologies, such as the well-known OpenAI ChatGPT and Microsoft Copilot, have sparked significant societal, economic, and regulatory challenges. Indeed, while the latter technologies promise unprecedented productivity gains, they also raise several concerns, such as job loss and displacement, deepfakes, and intellectual property violations. The present article aims to explore the present regulatory landscape of GenAI across the major global players, highlighting the divergent approaches adopted by the United States, United Kingdom, China, and the European Union. By drawing parallels with other complex global issues such as climate change and nuclear proliferation, this paper argues that the available traditional regulatory frameworks may be insufficient to address the unique challenges posed by GenAI. As a result, this article introduces a resilience-focused regulatory approach that emphasizes aspects such as adaptability, swift incident response, and recovery mechanisms to mitigate potential harm. By analyzing the existing regulations and suggesting potential future directions, the present article aims to contribute to the ongoing discourse on how to effectively govern GenAI technologies in a rapidly evolving regulatory landscape. Full article

Within the rapid integration of AI into educational settings, understanding its impact on essential cognitive skills is crucial for developing effective teaching strategies and improving student outcomes. This study examines the influence of generative artificial intelligence (GenAI) on students’ critical thinking and problem-solving skills in higher education. Our research specifically investigates how the perceived ease of use, usefulness, and learning value of GenAI tools might influence students’ critical thinking and problem-solving skills, and whether self-regulation serves as a mediator in this relationship. Utilising a quantitative approach, we surveyed 223 students and analysed their responses using a structural equation modelling method. The results reveal that the ease of use of GenAI significantly enhances self-regulation, which in turn positively impacts both the critical thinking and problem-solving abilities of students. However, the perceived usefulness and learning value of GenAI were not found to significantly influence these skills through self-regulation. These findings suggest that, while AI tools can offer an environment conducive to developing higher-order cognitive skills, this might not necessarily translate to the enhancement of students’ skills. This research contributes to the ongoing literature on the role of technology in education by highlighting the importance of designing GenAI tools that support self-regulated learning. Furthermore, it calls for educators and developers to focus not just on the functionality of AI, but also on how these tools can be integrated into curricula to effectively support critical thinking and problem-solving. The practical implications of our research highlight the need for AI tools that are user-friendly and aligned with educational goals, enhancing their adoption and effectiveness in improving student outcomes. It is crucial for educators to integrate strategies that promote self-regulation within AI-enhanced learning environments to maximise their impact on student learning. Full article

The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows. Full article

The increasing use of generative artificial intelligence (GenAI) has led to a rise in conversations about how teachers and students should adopt these tools to enhance the learning process. Self-regulated learning (SRL) research is important for addressing this question. A popular form of GenAI is the large language model chatbot, which allows users to seek answers to their queries. This article seeks to adapt current SRL models to understand student learning with these chatbots. This is achieved by classifying the prompts supplied by a learner to an educational chatbot into learning actions and processes using the process–action library. Subsequently, through process mining, we can analyze these data to provide valuable insights for learners, educators, instructional designers, and researchers into the possible applications of chatbots for SRL. Full article

The utilization of conversational generative artificial intelligence (Gen AI) in learning is often seen as a double-edged sword that may lead to superficial learning. We designed and implemented a programming course focusing on collaboration between students and Gen AI. This study explores the dynamics of such collaboration, focusing on students’ communication strategies with Gen AI, perceived benefits, and challenges encountered. Data were collected from class observations, surveys, final reports, dialogues between students and Gen AI, and semi-structured in-depth interviews. The results showed that effective collaboration between students and Gen AI could enhance students’ meta-cognitive and self-regulated learning skills and positively impact human-to-human communication. This study further revealed the difficulties and individual differences in collaborating with Gen AI on complex learning tasks. Overall, collaborating with Gen AI as a learning partner, rather than just a tool, enables sustainable and independent learning, beyond specific learning tasks at a given time. Full article

In Western tonal music, tension in chord progressions plays an important role in defining the path that a musical composition should follow. The creation of chord progressions that reflects such tension profiles can be challenging for novice composers, as it depends on many subjective factors, and also is regulated by multiple theoretical principles. This work presents ChordAIS-Gen, a tool to assist the users to generate chord progressions that comply with a concrete tension profile. We propose an objective measure capable of capturing the tension profile of a chord progression according to different tonal music parameters, namely, consonance, hierarchical tension, voice leading and perceptual distance. This measure is optimized into a Genetic Program algorithm mixed with an Artificial Immune System called Opt-aiNet. Opt-aiNet is capable of finding multiple optima in parallel, resulting in multiple candidate solutions for the next chord in a sequence. To validate the objective function, we performed a listening test to evaluate the perceptual quality of the candidate solutions proposed by our system. Most listeners rated the chord progressions proposed by ChordAIS-Gen as better candidates than the progressions discarded. Thus, we propose to use the objective values as a proxy for the perceptual evaluation of chord progressions and compare the performance of ChordAIS-Gen with chord progressions generators. Full article