https://reunir.unir.net/handle/123456789/16202 2024-10-29T14:04:33Z 2024-10-29T14:04:33Z Alier, Marc García-Peñalvo, Francisco Camba, Jorge D. https://reunir.unir.net/handle/123456789/16211 2024-03-12T13:44:15Z

Generative Artificial Intelligence in Education: From Deceptive to Disruptive Alier, Marc; García-Peñalvo, Francisco; Camba, Jorge D. Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:44:15Z No. of bitstreams: 1 Generative Artificial Intelligence in Education.pdf: 978561 bytes, checksum: 533e391d47ba81cd9a858d8b510f476a (MD5); Made available in DSpace on 2024-03-12T13:44:15Z (GMT). No. of bitstreams: 1 Generative Artificial Intelligence in Education.pdf: 978561 bytes, checksum: 533e391d47ba81cd9a858d8b510f476a (MD5)

Tang, Lin Su, Yu-Sheng https://reunir.unir.net/handle/123456789/16210 2024-03-12T13:30:39Z

Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom: A Systematic Literature Review Tang, Lin; Su, Yu-Sheng The increasing use of artificial intelligence (AI) models in the classroom not only brings a large number of benefits, but also has a variety of ethical implications. To provide effective education, it is now necessary to understand the ethical implications of using AI models in the classroom, and the principles for avoiding and addressing these ethical implications. However, existing research on the ethical implications of using AI models in the classroom is rather sparse, and a holistic overview is lacking. Therefore, this study seeks to offer an overview of research on the ethical implications, ethical principles and the future research directions and practices of using AI models in the classroom through a systematic literature review. Out of 1,445 initially identified publications between 2013 and 2023, 32 articles were included for final coding analysis, identified using explicit inclusion and exclusion criteria. The findings revealed five main ethical implications, namely algorithmic bias and discrimination, data privacy leakage, lack of transparency, decreased autonomy, and academic misconduct, with algorithmic bias being the most prominent (i.e., the number of existing studies is the most), followed by privacy leakage, whereas decreased autonomy and academic misconduct were relatively understudied; and six main ethical principles, namely fairness, privacy, transparency, accountability, autonomy and beneficence, with fairness being the most prominent ethical principle (i.e., the number of existing studies is the most), followed by privacy, while autonomy and beneficence were relatively understudied. Future directions of research are given, and guidelines for future practice are provided: (1) further substantive discussion, understanding and solution of ethical implications are required; (2) the precise mechanism of ethical principles of using AI models in the classroom remains to be elucidated and extended to the implementation phase; and (3) the ethical implications of the use of AI models in the classroom require accurate assessment. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:30:39Z No. of bitstreams: 1 Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom.pdf: 454767 bytes, checksum: 3722e4324633951952250b959168ccec (MD5); Made available in DSpace on 2024-03-12T13:30:39Z (GMT). No. of bitstreams: 1 Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom.pdf: 454767 bytes, checksum: 3722e4324633951952250b959168ccec (MD5)

Parra, Verónica Sureda, Patricia Corica, Ana Schiaffino, Silvia Godoy, Daniela https://reunir.unir.net/handle/123456789/16209 2024-03-12T13:21:58Z

Can Generative AI Solve Geometry Problems? Strengths and Weaknesses of LLMs for Geometric Reasoning in Spanish Parra, Verónica; Sureda, Patricia; Corica, Ana; Schiaffino, Silvia; Godoy, Daniela Generative Artificial Intelligence (AI) has emerged as a disruptive technology that is challenging traditional teaching and learning practices. Question-answering in natural language fosters the use of chatbots, such as ChatGPT, Bard and others, that generate text based on pre-trained Large Language Models (LLMs). The performance of these models in certain areas, like Math problem solving is receiving a crescent attention as it directly impacts on its potential use in educational settings. Most of these evaluations, however, concentrate on the construction and use of benchmarks comprising diverse Math problems in English. In this work, we discuss the capabilities of most used LLMs within the subfield of Geometry, in view of the relevance of this subject in high-school curricula and the difficulties exhibited by even most advanced multimodal LLMs to deal with geometric notions. This work focuses on Spanish, which is additionally a less resourced language. The answers of three major chatbots, based on different LLMs, were analyzed not only to determine their capacity to provide correct solutions, but also to categorize the errors found in the reasoning processes described. Understanding LLMs strengths and weaknesses in a field like Geometry can be a first step towards the design of more informed methodological proposals to include these technologies in classrooms as well as the development of more powerful automatic assistance tools based on generative AI. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:21:58Z No. of bitstreams: 1 Can Generative AI Solve Geometry Problems.pdf: 2743517 bytes, checksum: fe171389fc630d3009e3563d71f1ece1 (MD5); Made available in DSpace on 2024-03-12T13:21:58Z (GMT). No. of bitstreams: 1 Can Generative AI Solve Geometry Problems.pdf: 2743517 bytes, checksum: fe171389fc630d3009e3563d71f1ece1 (MD5)

Griffiths, Dai Frías-Martínez, Enrique Tlili, Ahmed Burgos, Daniel https://reunir.unir.net/handle/123456789/16207 2024-03-12T13:07:12Z

A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination Griffiths, Dai; Frías-Martínez, Enrique; Tlili, Ahmed; Burgos, Daniel The recent sudden increase in the capabilities of Large Language Models (LLMs), and generative AI in general, has astonished education professionals and learners. In formulating a response to these developments, educational institutions are constrained by a lack of clarity concerning human-machine communication and its relationship to models of education. Ideas and models from the cybernetic tradition can help to fill this gap. Two paradigms are distinguished: (1) the transmission paradigm (combining the model of learning implied by the instruments and processes of formal education and the conduit model of communication), and (2) the coordination paradigm (combining the constructivist model of learning and the coordination model of communication). It is proposed that these paradigms have long coexisted in educational practice in a modus vivendi, which is disrupted by LLMs. If an LLM can pass an examination, then from within the transmission paradigm this can only understood as demonstrating that the LLM has indeed learned and understood the material being assessed. At the same time, we know that LLMs do not in fact have the capacity to learn and understand, but rather generate a simulacrum of intelligence. It is argued that this paradox prevents educational institutions from formulating a coherent response to generative AI systems. However, within the coordination paradigm the interactions of LLMs and education institutions can be more easily understood and can be situated in a conversational model of learning. These distinctions can help institutions, educational leaders, and teachers, to frame the complex and nuanced questions raised by GenAI, and to chart a course towards its effective use in education. More specifically, they indicate that to benefit fully from the capabilities of generative AI education institutions need to recognize the validity of the coordination paradigm and adapt their processes and instruments accordingly. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:07:12Z No. of bitstreams: 1 A Cybernetic Perspective on Generative AI in Education.pdf: 547220 bytes, checksum: da8b47a5871354b7eada1c123edbb5cc (MD5); Made available in DSpace on 2024-03-12T13:07:12Z (GMT). No. of bitstreams: 1 A Cybernetic Perspective on Generative AI in Education.pdf: 547220 bytes, checksum: da8b47a5871354b7eada1c123edbb5cc (MD5)

Izquierdo-Domenech, Juan Linares-Pellicer, Jordi Ferri-Molla, Isabel https://reunir.unir.net/handle/123456789/16206 2024-03-12T12:54:12Z

Virtual Reality and Language Models, a New Frontier in Learning Izquierdo-Domenech, Juan; Linares-Pellicer, Jordi; Ferri-Molla, Isabel The proposed research introduces an innovative Virtual Reality (VR) and Large Language Model (LLM) architecture to enhance the learning process across diverse educational contexts, ranging from school to industrial settings. everaging the capabilities of LLMs and Retrieval-Augmented Generation (RAG), the architecture centers around an immersive VR application. This application empowers students of all backgrounds to interactively engage with their environment by posing questions and receiving informative responses in text format and with visual hints in VR, thereby fostering a dynamic learning experience. LLMs with RAG act as the backbones of this architecture, facilitating the integration of private or domain-specific data into the learning process. By seamlessly connecting various data sources through data connectors, RAG overcomes the challenge of disparate and siloed information repositories, including APIs, PDFs, SQL databases, and more. The data indexes provided by RAG solutions further streamline this process by structuring the ingested data into formats optimized for consumption by LLMs. An empirical study was conducted to evaluate the effectiveness of this VR and LLM architecture. Twenty participants, divided into Experimental and Control groups, were selected to assess the impact on their learning process. The Experimental group utilized the immersive VR application, which allowed interactive engagement with the educational environment, while the Control group followed traditional learning methods. The study revealed significant improvements in learning outcomes for the Experimental group, demonstrating the potential of integrating VR and LLMs in enhancing comprehension and engagement in learning contexts. This study presents an innovative approach that capitalizes on the synergy between LLMs and immersive VR technology, opening avenues for a transformative learning experience that transcends traditional boundaries and empowers learners across a spectrum of educational landscapes. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T12:54:12Z No. of bitstreams: 1 Virtual Reality and Language Models, a New Frontier in Learning.pdf: 2275154 bytes, checksum: 2dbd03feb08da813e2b458ff33488a46 (MD5); Made available in DSpace on 2024-03-12T12:54:12Z (GMT). No. of bitstreams: 1 Virtual Reality and Language Models, a New Frontier in Learning.pdf: 2275154 bytes, checksum: 2dbd03feb08da813e2b458ff33488a46 (MD5)

Bartlett, Kristin A. Camba, Jorge D. https://reunir.unir.net/handle/123456789/16205 2024-03-12T12:42:39Z

Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics Bartlett, Kristin A.; Camba, Jorge D. Image-generative artificial intelligence (AI) is increasingly being used in the product design process. In this paper, we present examples of how it is being used and discuss the possibilities of how applications may evolve in the future. We discuss the legal and ethical implications of image-generative AI, including concerns about bias, hidden labor, theft from artists, lack of originality in the outputs, and lack of copyright protection. We discuss how these concerns apply to design education and provide recommendations to educators about how AI should be addressed in the design classroom. We recommend that educators introduce AI as one tool among many in the designer’s toolkit and encourage it to be used as a process tool rather than for generating final design deliverables. We also provide guidance for how educators might engage students in discussions about AI to enhance their learning. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T12:42:39Z No. of bitstreams: 1 Generative Artificial Intelligence in Product Design Education.pdf: 2541510 bytes, checksum: 5ad68dd261b50c7854f81bbb23489f26 (MD5); Made available in DSpace on 2024-03-12T12:42:39Z (GMT). No. of bitstreams: 1 Generative Artificial Intelligence in Product Design Education.pdf: 2541510 bytes, checksum: 5ad68dd261b50c7854f81bbb23489f26 (MD5)

Rigaud Téllez, Nelly Rayón Villela, Patricia Blanco Bautista, Roberto https://reunir.unir.net/handle/123456789/16204 2024-03-12T12:18:25Z

Evaluating ChatGPT-Generated Linear Algebra Formative Assessments Rigaud Téllez, Nelly; Rayón Villela, Patricia; Blanco Bautista, Roberto This research explored Large Language Models potential uses on formative assessment for mathematical problem-solving process. The study provides a conceptual analysis of feedback and how the use of these models is related in the context of formative assessment for Linear Algebra problems. Particularly, the performance of a popular model known as ChatGPT in mathematical problems fails on reasoning, proofs, model construction, among others. Formative assessment is a process used by teachers and students during instruction that provides feedback to adjust ongoing teaching and learning to improve student’s achievement of intended instructional outcomes. The study analyzed and evaluated feedback provided to engineering students in their solutions, from both, instructors and ChatGPT, against fine-grained criteria of a formative feedback model that includes affective aspects. Considering preliminary outputs, and to improve performance of feedback from both agents’ instructors and ChatGPT, we developed a framework for formative assessment in mathematical problemsolving using a Large Language Model (LLM). We designed a framework to generate prompts, supported by common Linear Algebra mistakes within the context of concept development and problem-solving strategies. In this framework, the instructor acts as an agent to verify tasks in a math problem assigned to students, establishing a virtuous cycle of learning of queries supported by ChatGPT. Results revealed potentialities and challenges on how to improve feedback on graduate-level math problems, by which both educators and students adapt teaching and learning strategies. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T12:18:25Z No. of bitstreams: 1 Evaluating ChatGPT-Generated Linear Algebra Formative Assessments.pdf: 571024 bytes, checksum: c90c41bdd2f423882f07827c9f36cc78 (MD5); Made available in DSpace on 2024-03-12T12:18:25Z (GMT). No. of bitstreams: 1 Evaluating ChatGPT-Generated Linear Algebra Formative Assessments.pdf: 571024 bytes, checksum: c90c41bdd2f423882f07827c9f36cc78 (MD5)

Maslim, Martinus Wang, Hei-Chia Putra, Cendra Devayana Prabowo, Yulius Denny https://reunir.unir.net/handle/123456789/16203 2024-03-12T12:07:23Z

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method Maslim, Martinus; Wang, Hei-Chia; Putra, Cendra Devayana; Prabowo, Yulius Denny To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T09:23:17Z No. of bitstreams: 1 A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method.pdf: 1131436 bytes, checksum: ad08b7933bf4c2c45c3cb3530a9f79c6 (MD5); Made available in DSpace on 2024-03-12T09:23:17Z (GMT). No. of bitstreams: 1 A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method.pdf: 1131436 bytes, checksum: ad08b7933bf4c2c45c3cb3530a9f79c6 (MD5)