Abstract
Higher Education Institutions (HEIs) are increasingly incorporating artificial i ntelligence (AI) into their learning setup. In this paper, we analyze the results of a survey posed to 152 Higher Education (HE) students and 136 HE educators, of different scientific b ackgrounds, to emphasize the current incorporation of AI in the teaching and learning processes. The results reveal distinct viewpoints from both parties, reflecting diversified l evels o f e xperience, presumptions, and uneasiness. Thirty two percent of the teachers, completing the survey, confirms using AI. Approximately 50% reveal they notice their students using AI to (i) automate routine tasks in or out-ofclass, including check correctness of answers, obtaining real-time feedback; (ii) personalize learning tasks, such as write essays or projects and to illustrate them, and create presentations. A smaller percentage reveals students using AI to produce video content and contrast information learned in class. Alternative means, encompassing using AI at home, to study, to gather information, to sum up ideas in texts, are identified by most teachers as being employed by their students. Students using AI outnumber the teachers, though there are significant d ifferences in some responses, when compared to the teachers' perceptions, for the sames questions. Most of the students prefer AI to study at home, to obtain information to improve or to check an answer. Then a significant number does not exploit AI either to create presentations, write an essay or project, illustrate a project, producing videos, or to contrast information obtained in classes with that collected by AI tools. Regardless of these differences, both parties agree and strongly agree (with 79% of students and 86% of teachers) that AI will affect the HEIs educational process in the future. © 2024 IEEE.

Abstract
Sustainable agri-food systems seek to deliver food affordably and sustainably, without compromising the economic, social, and environmental bases for current and coming generations. Food-energy systems integrating renewable energy sources contribute towards this sustainability, and new solutions are being proposed in the literature or implemented in real facilities. This work reviews the existing literature on the integration of renewable energy, cross-sector energy efficiency and flexibility approaches, circular economy, digital solutions, and energy communities' (EC) structures within the agri-food sector. It proposes a formal classification of the main solutions found and describes the associated Business Models (BMs) to support their actual development cost-effectively. The main roles, actors and value propositions are reviewed, and a case example of an EC to be developed in Portugal in the Tools4AgriEnergy project is also described. The EC is based on floating PV panels to power water distribution pumps and share the surplus with local agri-food industries.

Abstract
The increasing number of Distributed Energy Resources (DERs) provides new opportunities for increased interactions between prosumers and local distribution companies. Aggregating large numbers of prosumers through Home Energy Management Systems (HEMS) allows for easier control and coordination of these interactions. With the contribution of the dedicated end-users in fulfilling the required flexibility during the day, the network operator can easily handle the power mismatches to avoid fluctuations in the load-generation side. The bi-level optimization allows for a more comprehensive and systematic assessment of flexibility procurement strategies. By considering both the network operator's objectives and the preferences and capabilities of end-users, this approach enables a more nuanced and informed decision-making process. Hence, this article presents a bi-level optimization model to examine the potential for several groups of prosumers to offer flexibility services to distribution companies. The model is applied to the IEEE 33 bus test system and solved through distributed optimization techniques. The model considers various DERs, including Battery Energy Storage Systems (BESS). Results show that the groups of aggregated consumers can provide between +/- 7 to +/- 29 kW flexibility in each interval, which is significant. Furthermore, the aggregators' flexibility capacity is closely linked to the demand at each node.

Abstract
This paper introduces GERF, a Gamified Educational Virtual Escape Room Framework designed to enhance micro-learning and adaptive learning experiences in educational settings. The framework incorporates a user taxonomy based on the user type hexad, addressing the preferences and motivations of different learners profiles. GERF focuses on two key facets: interoperability and analytics. To ensure seamless integration of Escape Room (ER) platforms with Learning Management Systems (LMS), the Learning Tools Interoperability (LTI) specification is used. This enables smooth and efficient communication between ERs and LMS platforms. Additionally, GERF uses the xAPI specification to capture and transmit experiential data in the form of xAPI statements, which are then sent to a Learning Record Store (LRS). By leveraging these learning analytics, educators gain valuable insights into students’ interactions within the ER, facilitating the adaptation of learning content based on individual learning needs. Ultimately, GERF empowers educators to create personalized learning experiences within the ER environment, fostering student engagement and learning outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them.

Abstract
This paper introduces a new method of Automated Program Repair that relies on a combination of the GPT-4 Large Language Model and automatic type checking of Haskell programs. This method identifies the source of a type error and asks GPT-4 to fix that specific portion of the program. Then, QuickCheck is used to automatically generate a large set of test cases to validate whether the generated repair behaves as the correct solution. Our publicly available experiments revealed a success rate of 88.5% in normal conditions. However, more detailed testing should be performed to more accurately evaluate this form of APR.