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AI Literacy Profile of Education Faculty Students: Sample of Dicle University

2025-07-28T05:34:24+00:00

This study examines AI literacy among undergraduate students at a public Turkish university. It describes current AI literacy of pre-service teachers and emphasizes the need for AI literacy inclusion in teacher training to enhance professional knowledge and skills. The survey research method was used with a quantitative approach, with a sample of 375 participants, identified through cluster sampling method. The AI Literacy Scale comprising 31 items was the data collection tool, with a 7-point Likert structure. For data analysis, normal distribution and homogeneity were checked with SPSS, the significance value was determined as 0.05, and descriptive statistics, t-test, Man Whitney U, ANOVA, Welch’s ANOVA, Kruskal Wallis, Scheffe, and Dunn tests were also measured. The findings reveal that 95% of the participants had no AI course; 79% were female; 38% studied Turkish & Social Sciences; 23% Mathematics & Science, and 16% Foreign Languages; and 23% studied only upto Primary Education. The results also reveal that participants had approximately medium AI literacy, the subject studied had a small impact on AI literacy but gender did not. Foreign Language departments showed the lowest AI literacy, possibly due to individual differences like interest and motivation. The AI literacy levels can be increased by adding AI courses to the curriculum and improving instructors’ knowledge and skills in integrating AI tools into courses.

Application of Machine Learning to Predict and Explain University Academic Performance

2025-07-28T05:50:35+00:00

In the educational field, academic performance represents the outcomes of evaluation processes and is related to students’ learning achievements. Early identification of the factors that influence performance allows for timely interventions to prevent course repetition and student dropout. In this regard, the objective of this study was to apply machine learning models to predict and explain academic performance, with a particular focus on students with a history of failing at least one course. A quantitative approach was used, with a non-experimental, ex post facto design, based on a population of 12,211 university students. Data were collected through a 32-item questionnaire covering sociodemographic, socioeconomic, emotional, institutional-academic, self-efficacy, and self-control aspects, linked to the student enrollment system, as well as an institutional database with seven academic variables. Three supervised classification algorithms were trained: Random Forest, XGBoost, and CatBoost. In addition, the SHAP method was used to interpret the model’s outputs. Data processing and analysis were conducted using Python in the Google Colab environment. CatBoost showed the best performance, achieving a 70% recall for the “failed” class. The most influential indicators were faculty, academic program, academic level or cycle, emotional state, teacher support, and previous academic performance. It is concluded that academic failure is influenced primarily by institutional-academic variables, followed by emotional, sociodemographic, and socioeconomic factors. The value of interpretable machine learning (SHAP) is highlighted as a tool to support educational decision-making.

Impact of AI on the Teaching of Graphic and Interior Design

2025-07-28T06:07:14+00:00

Artificial intelligence (AI) is transforming design education, impacting creativity, innovation, and learning processes in higher education. This study examines how students in Graphic Design and Interior Design, with different levels of AI experience, perceive and utilize these technologies in their academic training. A mixed-methods approach with a descriptive and comparative design was employed. Surveys were conducted with two groups: third-year Graphic Design students, who had prior AI experience, and first-year Interior Design students, who received a basic introduction. The questionnaires included quantitative questions on AI usage, impact, and perception, as well as open-ended questions to gather qualitative insights. Data were analyzed using descriptive statistics and content analysis, allowing for the identification of usage patterns, perceived benefits, and limitations. The results show that advanced students use AI critically and strategically, whereas beginners rely on more accessible tools for idea visualization. Both groups expressed concerns about creativity and highlighted the need for further training. The study concludes that AI holds great potential for design education, but its implementation must balance efficiency with the development of critical thinking and creativity.

Integrating STEM and HAS for AI Literacy: An Interdisciplinary Model for Higher Education

2025-07-28T06:13:10+00:00

The growing adoption of generative artificial intelligence (AI) in higher education requires innovative approaches that combine technical and reflective skills. Most academic programs emphasize technical training, leaving aside critical, ethical and social aspects of AI. This study seeks to investigate how the integration between STEM (Science, Technology, Engineering and Mathematics) and HAS (Humanities, Arts and Social Sciences) can strengthen literacy in AI, promoting a more holistic and interdisciplinary teaching. Using an approach to mixed methods, we perform a bibliometric analysis of 100 academic articles (Scopus, Web of Science and Google Scholar), in addition to semi structured interviews with 20 teachers and researchers specialized in STEM, you have generated. The data were statistically analyzed and according to the thematic category, allowing identifying benefits, challenges and strategies for interdisciplinarity in the teaching of AI. The results indicate that interdisciplinary collaboration strengthens transversal skills such as critical thinking, creativity and ethical decision-making, essential for the responsible development of AI. Challenges such as the lack of integrated curricular structures and institutional resistance for the implementation of said educational model were identified. In response, an interdisciplinary model of literacy in AI is proposed, which can guide universities in the training of professionals capable of working in multidisciplinary teams in governance and development of AI.

Supporting Generative AI Literacy: Exploring the Pedagogical Roles Students Assign ChatGPT and Impact on Course Grades

2025-07-28T06:26:24+00:00

This research examines generative AI (GenAI) use in a university course that encouraged ChatGPT for specific assignments. Using the Pedagogical Centered AI (PCAI) framework, we explore how students perceive, use, and position ChatGPT, and how usage patterns influenced performance. Students utilized ChatGPT during the latter half of the Spring 2024 semester. Comparisons were made with the first half of the course and prior iterations (2022 and 2023) without GenAI. All students in the 2024 cohort — 40 students — were invited to participate in the study. Data includes 18 student interviews from the 2024 cohort and student work from all iterations. Interviews underwent qualitative deductive thematic analysis using PCAI’s predefined codes PCAI frames AI in education through six learning theories: behaviorism, cognitivism, constructivism, social constructivism, experiential learning, and communities of practice. Class materials and academic records were analyzed to assess performance quantitatively using inferential statistics. Findings reveal students predominantly view AI from a behaviorist perspective: as a tool for completing tasks. Some aligned usage with cognitive learning theory by using AI to reduce cognitive load, while others adopted social constructivist or constructivist perspectives, using AI to build understanding through feedback and exam preparation functions. Overuse of ChatGPT correlated with lower grades, though only one student acknowledged its negative impact on learning. We discuss implications for higher education and highlight how ChatGPT supports diverse teaching and learning approaches tailored to students’ needs. In particular, strategies aligned with constructivism, social constructivism, and communities of practice approaches seem to enhance student learning. However, behaviorist approaches to AI use could hinder learning outcomes. Although most students were aware of the negative impact of AI overuse, they also mentioned that minimal training and explanation were provided in other classes, highlighting the need for a more comprehensive program to support AI literacy in higher education.

Pedagogy without generative AI - Degree in Computer Science without Humanities

2025-07-28T06:43:36+00:00

In 2019, UNESCO, through the Beijing Consensus, highlighted that artificial intelligence (AI) will increasingly have a huge impact on various sectors of society. Academics such as Thomas Chiu, along with other researchers (2025), believe that most of the population will learn and work with artificial intelligence and argue that it is crucial to teach AI in basic education. The Economic Forum (2025) points out that skills related to artificial intelligence and big data are the fastest growing. According to Russell y Norvig (1995), AI is a field that encompasses several subfields, in addition to inheriting knowledge from other disciplines. Given the emerging need to learn AI in formal education, starting in primary school, this study has verified whether undergraduate education programmes in Brazil are preparing future teachers for the use of AI in teaching. At the same time, based on the principle that AI is a field that encompasses different subfields, we investigated whether undergraduate computer science courses offer multidisciplinary training. To this end, the curricula of the three best education and computer science faculties in Brazil were analysed. According to the results, there is a high degree of specialisation, which makes interdisciplinarity or multidisciplinarity urgent in both courses.

Fear of Migrants and Rejection of Environmental Initiatives: Analysis of the VOX Party’s Discourse On the Social Network X

2025-07-28T06:53:55+00:00

The Digital Media Observatory of Spain and Portugal, promoted by the European Commission, categorizes false news content into the following themes: politics and elections, health, environment, migration, gender, and security. The consequences of the rise in disinformation are severe, as social polarization extends across all spheres and may be exacerbated by certain hostile or extremist political discourses. This research focuses on analyzing the discourse of the Spanish political party VOX on the social media platform X, with a specific emphasis on two issues: migration and sustainable development. The criminalization of individuals with irregular administrative status and criticism of policies related to the 2030 Agenda are central themes in the party’s discourse, justifying their selection for a joint study. The objective of this research is to analyze the presence of hostility in messages related to these topics, considering publication frequency, employed terminology, and framing strategies. The methodological approach is based on critical discourse analysis (Van Dijk, 1997), and the study period encompasses several electoral processes that took place in Spain over the past year. The sample consists of 696 posts from various VOX-affiliated accounts. The findings indicate that migration is the primary focus of the party’s discourse on this social media platform, characterized by a strong opposition to migrants and their culture. Regarding sustainable development, the party adopts a firm stance against European decisions, often without providing substantive arguments.

Evaluation of Educational Documentary using GAI and Prompt Engineering

2025-07-28T07:07:55+00:00

The study explores the use of generative artificial intelligence (GAI) in evaluating educational audiovisual material, focusing on the documentary NOMADS, produced at the Cabo de la Huerta Secondary School in Alicante, Spain, as part of an Erasmus+ project on human rights. A multi-agent system was developed using prompt engineering (PE) techniques in ChatGPT 4.0, aiming for a multidimensional evaluation of the documentary based on key performance indicators. The methodology is grounded in semiotic engineering, a discipline that analyzes human-computer interaction. The process comprises four phases and incorporates a variant of the Delphi method, a structured technique for achieving expert consensus. In the first phase, PE generates evaluation metrics. Then, expert agent profiles are defined. The third phase models the iteration of prompts to gather evaluations, and the final phase involves the statistical validation of results. Adaptive interaction factors are introduced as tools to map multidimensional evaluations using PE, integrating insights from five experts in education and audiovisual fields. The resulting metrics successfully captured elements such as educational impact, transmedia storytelling, and audiovisual quality. The outcome presents a validated methodological framework, with internally confirmed results through different coefficients, highlighting the need for further validation through both quantitative tools and real expert panels.

AI Capabilities in Formative Assessment of Undergraduate Theses: Human Experts vs GPT

2025-07-28T07:19:17+00:00

The accelerated development of artificial intelligence (AI) in educational contexts poses significant challenges to traditional models of formative assessment in Higher Education. This study aimed to examine the capacity of Generative Pretrained Transformer (GPT) models to perform evaluative functions in undergraduate thesis assessment, comparing their judgments with those issued by expert human evaluators. A non-integrated mixed-methods approach was employed, combining a quasi-experimental time-series design with a control group and a qualitative corpus analysis of feedback content. Sixteen undergraduate theses were intentionally selected as case studies and assessed at three successive stages of the formative process. The quantitative component analyzed the evolution, consistency, and alignment of scores, while the qualitative analysis explored the critical depth, argumentative structure, and pedagogical orientation of the feedback. The findings reveal a progressive convergence between GPT and expert evaluations, with high levels of correlation and agreement in the final stage. Additionally, GPT-generated feedback showed sustained improvement in semantic richness, argumentative precision, and adaptive capacity. It is concluded that, under controlled conditions and clearly defined evaluative criteria, GPT models exhibit significant potential as complementary agents in formative assessment within Higher Education, offering efficiency, consistency, and scalability. However, limitations remain regarding the personalization of feedback and the promotion of critical reflection, highlighting the need to enhance their pedagogical and metacognitive capabilities.

Knowledge and Teaching with Artificial Intelligence: Stem Vs. Humanities

2025-07-28T07:29:53+00:00

Artificial intelligence (AI) literacy has become an essential competency in higher education across disciplines, yet the teaching approaches and content requirements differ significantly between STEM and humanities fields. This mixed-methods study investigates these differences, focusing on the pedagogical strategies, AI literacy needs, and institutional gaps that exist between the two domains. A quasi-experimental design was applied using a structured questionnaire with 25 university students (12 from STEM and 13 from humanities). Quantitative data were analyzed through descriptive statistics, while qualitative data were examined using thematic analysis. The findings reveal that STEM students prioritize technical skills such as programming and algorithmic logic, whereas humanities students emphasize conceptual understanding, ethical reasoning, and the social impact of AI. Both groups express concern over insufficient institutional support for comprehensive AI training. The study identifies the need for adaptable, discipline-specific AI curricula and advocates for interdisciplinary learning environments that balance technical and ethical components. This research fills a gap in current literature by empirically comparing AI literacy frameworks across distinct academic traditions and proposes evidence-based recommendations for inclusive AI curriculum development.

Fostering Algorithmic Literacy in Education: Navigating News Ecosystems for Critical Media Understanding

2025-07-28T07:53:25+00:00

In an era of algorithmically curated news feeds, the interplay between technology and human behavior is transforming global information consumption. This study systematically reviews literature from 2015 to 2024, examining algorithms’ dual role as enhancers of personalization and drivers of polarization. It investigates how algorithmic bias influences news diversity, the effects of algorithmically driven news exposure on polarization, and the potential of media literacy to mitigate these impacts. The findings reveal a complex relationship between algorithmic curation, user behavior, and polarization, often exacerbated by system opacity. While algorithms can broaden exposure to diverse perspectives, they frequently reinforce existing beliefs through filter bubbles and echo chambers. Media literacy emerges as a vital tool, equipping individuals to critically engage with content and challenge biases. Addressing a growing research gap, this study explores the intricate dynamics between algorithmic personalization, polarization, and media literacy, proposing an educational framework to equip learners for AI-driven news environments. The proposed framework interconnects algorithmic curation, news exposure, user agency, media literacy, and polarization, emphasizing their cyclical dynamics. This research calls for algorithmic transparency, cross-cultural media literacy programs, and targeted studies in underrepresented regions, offering actionable pathways to support healthier public discourse through including algorithmic literacy in education.

AI and Marketing: Bridging the Gap Through a Game-based Tool Among Higher Education Students

2025-07-28T07:59:48+00:00

Artificial Intelligence has a very important impact on a company’s marketing strategy. From personalization, decision-making or content development, there are several value-creation instruments that can increase the notoriety of a brand. However, literature shows that Higher Education students do not fully understand the opportunities and threats that AI applied to Marketing has on their future jobs. This paper describes the conceptualization and validation of a game-based tool to promote knowledge about AI applied to marketing. Aimed at Higher Education Students who are about to enter the job market, this solution was designed to increase users’ critical sense of the opportunities and dangers of using automatic technologies. Four co-creation sessions were organized using a Design Thinking approach to increase participation and creativity of all participants. Trailblazer AI, a prototype of a boardgame with 300 questions, was produced in the following categories: AI Marketing Fundamentals; Tools and Platforms; Ethics and Privacy; Metrics and Analysis; Case Studies and Success Stories; and AI Powered Content Creation. The prototype was validated with a different group of 25 students. The results suggest that game-based tools developed with students in co creation environments can promote motivation and knowledge in AI applied to Marketing. About 92,00% of the respondents consider it as a good tool to promote knowledge about AI, and 72,00% of the students are revealed to be motivated to play the game in recreative contexts.

Use of Artificial Intelligence Applications in Higher Education in Spain

2025-07-28T08:09:35+00:00

Artificial intelligence has gained prominence and greater relevance in recent years, highlighting its innovative role in the educational sectors, especially in university higher education: management, pedagogy and didactics, etc. To survey the knowledge and use of artificial intelligence tools in the university population in Spain. A triple methodology was designed, with a descriptive phase on a sample of 335 teachers in higher university education chosen by simple random sampling on a total sample of 750 students, proposing three objectives referred to in three phases, a phase of association of variables such as the sex of teachers and the positive scores obtained in the analysis of the answers, and a contrast of hypotheses about determining the differences between male and female teachers. Two hypotheses are formulated in the second and third phases. The rational intentionality is based on the supposed lack of knowledge of artificial intelligence tools among teachers in higher university specialization training through official master’s degrees. It shows the lack of knowledge and use of a large part of the tools studied, the existence of a high degree of association between the sex variables and the positive scores referred to and the statistical conclusion that female teachers have a more positive component with respect to AI tools than male teachers. Most of the relevant authors consulted maintain the positivity of the knowledge and use of AI in the classroom, although others, however, appeal to legality and ethics as debatable and controversial elements.

The Use of ChatGPT in the Professional Training of Communication and Digital Marketing Students: Student Perceptions in Costa Rica

2025-07-28T08:16:29+00:00

This study analyzes the use of ChatGPT in the professional training of university students in the fields of communication and digital marketing through a case study focused on Costa Rica. A quantitative approach was adopted, applying an online survey to students from the Universidad Latina de Costa Rica (ULATINA), with the aim of identifying usage patterns of the tool, its strategic applications in communication and marketing processes, and students’ perceptions of its contribution to critical thinking, creativity, and digital planning. The results show a high adoption of ChatGPT among Costa Rican students, particularly in tasks related to content creation, strategic planning, and data analysis. However, tensions also emerge related to trust, technological dependency, and the potential devaluation of key professional skills. These findings reveal a particular reality within the Costa Rican university model, where the intensive use of generative artificial intelligence coexists with ambivalent perceptions about its actual contribution to the quality of education. The study concludes by highlighting the need to develop pedagogical strategies that promote critical, ethical, and contextualized use of these tools in higher education in Costa Rica.

University Training in Digital Journalism and Artificial Intelligence in Ecuador: Competencies, Challenges, and Pedagogical Proposals

2025-07-28T08:23:56+00:00

Artificial intelligence (AI) is revolutionizing journalism by demanding new technical competencies and ethical principles. In Ecuador, these technological changes have not yet been fully integrated into the academic curricula of higher education institutions, creating disparities between the demands of the communication sector and professional training. This study highlights the need to update and align curricula with the needs of the digital sector, where technology becomes a transformative axis. Unlike previous research, this work combines curriculum analysis with the insights and experiences of students and professors regarding the implementation of AI, offering a proposal that integrates ethics, education, and technology. A mixed-methods approach was used, collecting data through surveys of 269 students from Communication and Journalism programs at ECOTEC and UEES universities, complemented by interviews with experts who contributed to the subject. The results show a low level of familiarity with AI tools, mistrust in their content, and minimal presence of these topics in the curricula. Tools such as ChatGPT, Midjourney, Aiornot, and Studio D-ID are important resources with potential for journalistic practice, yet they remain largely unknown and underutilized by students. It is concluded that there is a pressing need to update teaching strategies, implement practical learning environments, and foster partnerships between academia and the communication industry to ensure a comprehensive and relevant education for today’s society.

Trends in the Use of Artificial Intelligence (IA) as an Auxiliary Tool in the Academic Process of Licenciatura en Ciencias Ambientales (LCE) de la Universidad Autónoma de Guerrero (UAGro)

2025-07-28T08:36:05+00:00

In recent years, higher education has faced the challenge of adapting to the changes demanded by the evolution and development of AI usage, due to the accessibility and user-friendly design, generating a high increase in the number of users employing these tools for educational purposes. This study emerges from the absence of precedent within the Universidad Autónoma de Guerrero (UAGro) regarding the academic use of AI and its trends among university students. Consequently, the research aims to underscore the critical role of the educational sector in promoting a guided and pedagogically sound integration of AI into the classroom. Such guidance is essential to prevent the misuse of AI in ways that may undermine cognitive development and interfere with the instructional design of academic activities. The objective of this research was to examine the current trends in AI usage among Environmental Science undergraduate students at UAGro, establishing a foundational reference for future inquiry. A quantitative, applied, and cross-sectional methodological approach was employed. Data were collected via a structured survey comprising 31 questions organized into six thematic categories. Results revealed that 78.5% of students currently incorporate AI into their academic activities. Notably, 81% indicated they had received no formal instruction on AI use, relying instead on self-directed learning. The most common AI-supported activities identified were information retrieval, idea generation, and text translation, concluding that students are already using AI in their studies without any guidance from the education sector and highlighting positive and ethical stances in AI usage trends.

Artificial Intelligences in the Classroom: A Critical View on Reliability and Credibility

2025-07-28T08:46:01+00:00

Generative Artificial Intelligence (GAI) tools, such as ChatGPT, are transforming higher education. This study critically examines their usefulness from a student’s perspective, distinguishing between reliability (alignment with scientific evidence) and credibility (users’ subjective trust). The experience was carried out at Miguel Hernández University during the 2024/2025 academic year, within the course “Healthcare Quality” in the Occupational Therapy degree program. The methodology combined the collaborative design of a multimedia glossary using GAI, combined with both formal (structured questionnaire) and informal (Padlet) assessments. The results revealed high levels of satisfaction, meaningful learning, and critical thinking development. The most frequently used tools were ChatGPT, Canva, Suno, and Sora. Students distinguished between credible information—valued for its clarity and personalization—and reliable information—validated through comparison with course materials or scientific sources. The study concludes that GAI tools can enrich learning when integrated through a critical and pedagogical approach, maintaining the instructor as an essential mediator of knowledge. This work proposes transferable practices and a teaching decalogue aimed at fostering critical GAI literacy in higher education, grounded in an empirical and innovative framework.

Reimagining Education in the Age of AI. An Ecosocial and Cosmotechnical Approach from a Critique of Digital Extractivism

2025-07-28T09:01:30+00:00

This article presents a critical reflection on the role of artificial intelligence (AI) in education from an ecosocial and cosmotechnical perspective. It aims to contribute to a critical technological literacy that highlights the ecological, social, and epistemic impacts of these technologies. Drawing on a situated pedagogical experience developed within an international project on immersive technologies, the paper explores how AI reshapes the relationships between body, knowledge, and environment. It argues for the need to imagine alternative forms of educational AI integration, moving away from dominant technocentric and extractivist approaches. Rather than viewing AI as a neutral or inevitable solution, the article defends the pedagogical value of error, imagination, material engagement, and artistic creation as practices of resistance and knowledge generation. Methodologically, the study combines conceptual review and interpretive analysis, employing a research approach that intertwines theory, narrative, and creative practice. This work addresses the lack of critical and situated perspectives in current educational AI discourse, and offers tools to reimagine technology through care, teacher agency, and epistemic justice.

Collaborative Algorithmic Creations between Humans and Machines: Studies of Automatic Image Composition based on Artificial Intelligence for Art Education

2025-07-28T09:08:47+00:00

Technological developments in AI foster creativity in the world of liquid modernity in education. As an introduction, the relationship between AI and previous educational experiences is analysed, as well as how the creation of images with generative models in AI has evolved. This article presents a practice based on images created with AI generators and their application in the early childhood education degree programme. The methodology used is quantitative, with Likert surveys to verify its applicability and a description of the activity in a portfolio. Before designing the AI practice, similar experiences with AI in art and education are reviewed to see if it is an appropriate medium. The overall objective of the project is to analyse the impact and creative possibilities of using AI to create children’s storybook covers as a teaching resource in teacher training. Among the specific objectives, AI tools for illustrating stories are explored and critical thinking about the construction of these images is promoted, not as a final art form, but based on the process. The result shows an educational experience implemented in the early childhood education degree programme with the creation of children’s book covers. I conclude that AI is a resource that provides skills and allows for the exploration of alternatives in the process of creating an image.

Meaningful Learning in Generative AI Environment: Gamification for Educational Experiences

2025-07-28T09:26:45+00:00

This transdisciplinary study examines the impact of innovative methodologies in educational environments enhanced by generative artificial intelligence and gamification across seven Ibero-American countries (Ecuador, Spain, Bolivia, México, Argentina y Puerto Rico), involving 2,800 university students. Applying the SIALU methodology (Innovative System for University Playful Learning), Transmedia Open Online Courses (TOOCs) were designed, integrating affective intelligent virtual agents (AVIAs), project-based learning, and adaptive gamification. The main objectives include analyzing the effectiveness of these environments in student motivation, digital inclusion, and the development of transversal competencies. Using a mixed-methods approach (quantitative and qualitative), data were analyzed through focus groups, learning logs, and photographic records. The results indicate an 85% improvement in content comprehension (p<0.001) and a 90% increase in student participation, highlighting the impact of transmedia narratives and digital fanzines on cultural and creative resignification. Additionally, 78% of participants reported significant progress in creative and semiotic competencies, demonstrating the potential of these tools to transform learning in higher education. These findings underscore the need to integrate exponential technologies, such as generative AI, ubiquitous and affective computing, extended reality systems, and the metaverse into curriculum design. These innovations foster motivation and digital inclusion while optimizing academic performance, establishing an adaptable and scalable educational model to meet the challenges of the digital era.