Academic literature on the topic 'AI-driven teaching strategies'

Computer vision and biometrics are increasingly important in many AI-driven applications, yet teaching these fields poses challenges in balancing theory and hands-on practice. This paper presents a structured approach implemented for the technical skills course at the Faculty of Computer and Information Science, University of Ljubljana, designed for Computer Science students. The course integrates guided Jupyter Notebook exercises while allowing students to complete coding tasks while leaning on AI assistance. In-person presentations and discussions reinforce understanding by requiring students to explain their implementations and problem-solving strategies. The 15-week curriculum progresses from basic image processing to deep learning-based biometric recognition. Teaching materials are derived from the AIM@VET EU project, which focuses on adapting AI education to labor market needs, but adapted here for university students. We hope that AI-assisted, structured coding exercises combined with interactive discussions will enhance engagement and comprehension, better preparing students for a variety of applications in computer vision and biometrics.

Native corn production in Tlaxiaco, Oaxaca, is an ancestral practice deeply rooted in the region’s cultural and gastronomic identity. However, producers face significant challenges, such as limited technological access and high illiteracy rates, which hinder their ability to diversify income through sustainable experiential tourism. This research proposes an Artificial Intelligence (AI)-based teaching model that facilitates the training of native corn producers, enabling them to transform their agricultural products into culinary tourism experiences. The program leverages AI to create contextualized educational manuals, adaptive learning strategies, and interactive content, allowing producers to acquire essential skills without requiring literacy or advanced digital proficiency.The main objective is to empower the community, strengthen regional economies, and preserve cultural heritage by integrating AI-driven educational tools with traditional knowledge. Specific objectives include: (1) designing accessible manuals for teaching traditional gastronomic processes, (2) applying AI tools to generate visual and auditory learning materials, (3) training producers in preparing and presenting traditional dishes for tourists, (4) developing strategies for experiential tourism and sustainable commercialization, and (5) evaluating the program’s impact based on community participation and entrepreneurial initiatives.The methodology consists of three phases, tailored to the community’s socio-cultural conditions:Diagnosis and Development of Educational Materials – Conducting interviews with producers to assess prior knowledge and challenges, designing printed manuals with illustrated content and AI-generated audio recordings, and producing multilingual instructional videos with AI-assisted narration.Gastronomic Training for Experiential Tourism – Organizing hands-on workshops where producers learn to prepare and present traditional dishes, such as masita (corn-based dishes with beef or lamb), tasajo, chorizo, and machucadas (corn tortillas with regional chilies). The training integrates multisensory learning techniques, including tastings, live demonstrations, and guided cooking experiences for tourists.Implementation of the Sustainable Tourism Model – Designing interactive tourism activities such as guided visits to native corn fields, immersive traditional cooking classes, and tastings highlighting the distinct characteristics of native corn products. AI-powered market analysis will be used to optimize pricing and promotional strategies, ensuring sustainable commercialization.AI is applied in three key areas:Educational Content Creation – AI-generated illustrated manuals, multilingual audio guides, and interactive instructional videos.Personalized Learning – AI-powered virtual assistants answering producers’ questions, interactive diagrams explaining food preparation, and adaptive learning strategies tailored to different knowledge levels.Market Optimization – AI-driven analysis of tourism trends, pricing recommendations, and product promotion strategies based on gastronomic tourism data.The expected outcomes include training at least 50 producers in the first year, developing accessible educational resources, launching gastronomic workshops as a tourism product, strengthening the regional economy through food commercialization, and preserving traditional culinary knowledge for future generations. The initiative also aims to establish a sustainable tourism route in Tlaxiaco, showcasing native corn production and traditional cooking practices.The social and sustainability impact of this project is substantial. It fosters social inclusion by making training accessible to producers with varying literacy levels, empowers rural communities by promoting entrepreneurship, encourages responsible and sustainable tourism, and supports agroecological conservation efforts to protect native corn varieties.This initiative represents a pioneering effort to bridge traditional knowledge with emerging AI-driven educational technologies, creating a replicable and scalable model for rural development. By integrating AI into the learning process and sustainable commercialization of traditional foods, the project offers an innovative pathway for empowering indigenous communities, preserving cultural heritage, and promoting sustainable tourism in Tlaxiaco and beyond.

This article articulates the nuanced challenges of integrating Generative AI (GenAI) into educational settings, aiming to dispel overly simplistic narratives driven by unwarranted enthusiasm or unfounded apprehensions. It introduces a conceptual framework for the application of GenAI within higher education, delineating four key strategies that leverage the dual roles of educators—as both creators and designers—while positioning GenAI as either a facilitative agent (creature) or a utilitarian tool. The identified strategies—Interactive Co-Creation, Adaptive Design, Learning Scaffold, and Efficient Structuring—underscore GenAI’s potential to revolutionize teaching methodologies by enabling personalized education, enhancing content quality, and expediting course development processes. Emphasizing GenAI’s capacity to cater to diverse student needs, simplify educational content creation, and foster engaging learning environments, the model provides educators with a roadmap for integrating GenAI into their instructional practices to harness the full potential of educational technology.Generative AI; educator’s role; instructional design

This study explores the use of artificial intelligence (AI) as an educational tool to train rural communities in sustainable development and microenterprise creation. The initiative aimed to strengthen local knowledge on the sustainable use of native species, particularly Atractosteus tropicus (tropical gar), and to foster economic growth through the development of innovative products and the creation of small businesses. The communities were trained on the importance of conserving local biodiversity while sustainably utilizing these natural resources for economic purposes.AI played a key role in creating customized educational training materials designed to meet the needs and cultural context of the communities. These instructional materials focused on sustainable fishing practices as well as the development of products derived from this local species. AI helped bridge the gap between existing community practices and new sustainable approaches by applying traditional knowledge with personalized teaching techniques. This integration empowered communities to balance environmental management with economic opportunities.A fundamental component of the program was the development of entrepreneurial skills. Participants, many of whom were women, received training in microenterprise creation, branding, and marketing strategies. AI-generated materials guided them in creating value-added products, such as tropical gar tamales and empanadas, which could be marketed locally and regionally. The hands-on approach, which included financial management and sustainable production, provided participants with a solid foundation to establish and grow their businesses.The training aimed to empower community members by providing them with the necessary tools to effectively start and manage microenterprises. Additionally, the AI-driven approach facilitated financial education, enabling participants to acquire business management practices that support long-term economic sustainability. This approach combined biodiversity conservation with business development, fostering a greater understanding of how environmental care can coexist with economic growth.Preliminary results indicate that community members gained confidence in applying sustainable practices and creating new businesses, expressing through interviews an interest in continuing to participate in similar projects that address socioeconomic development through the sustainable use of natural resources. The project reinforced a sense of belonging and responsibility towards natural resources within the community, and the trainers expressed interest in continuing to use AI-driven methods to enhance their skills. The success of this project demonstrates that AI can be a powerful tool for promoting sustainable development in rural areas, particularly when combined with education and discipline in management, community participation, and the protection of native species.Future research will focus on the long-term effects of these educational initiatives on the economic and environmental outcomes of the communities. The potential to expand this approach to other rural areas facing similar challenges will also be explored, with the aim of contributing to the Sustainable Development Goals (SDGs) related to environmental conservation and the economic empowerment of rural communities.

The field of education is constantly evolving, and educators continually seek innovative methods to enhance teaching and learning experience. In recent years, the convergent approach to didactics has emerged as a promising paradigm. This approach involves the seamless integration of diverse pedagogical techniques, strategies, and cutting-edge technologies to forge a cohesive and highly effective learning environment. This paper explores the the central challenges that arise during the transition to this new educational paradigm, driven by the convergence of network technologies. Particular attention is devoted to the integration of artificial intelligence (AI) within the educational domain. It delves into the taxonomy of goals and teaching methodologies intrinsic to this transformative shift. A special emphasis is placed on the noteworthy contributions of ChatGPT to the realm of language education, shedding light on its unique capabilities and potential impact. Based on the existing literature analysis, this paper highlights the critical facets to consider when incorporating AI-powered technologies into the educational landscape.

With the proliferation of computing technologies and an ongoing trend of introducing digital and blended learning aspects into higher education, innovative approaches to teaching complex topics like artificial intelligence (AI) have emerged. Of particular interest is the use of game-based learning approaches. According to problem-based learning theory, providing students with an interactive problem and encouraging them to independently find solutions promotes deeper understanding and skill acquisition. Thus, game-based approaches offer an engaging way for students to explore challenging concepts. However, despite the growing use of game-based methods in fields like economics, their application in computer science - especially in teaching game AI - remains under-explored.Understanding AI is increasingly critical for game development, as modern games emulate human-like behaviour in areas such as decision-making, character routines, and adaptive strategies. While many mechanisms and approaches of agent-level decision-making and planning are well understood, their application in video games poses unique challenges, such as accommodating unpredictable player interactions and ensuring performance efficiency without degrading the player experience. While strategy games like StarCraft or multiplayer online battle arenas like DotA 2 have been of interest as proving grounds for advanced AI training methods, their use in education has been limited due to their high complexity and associated learning curve.This work proposes the development of a novel interactive application to fill this niche. Taking inspiration from city building and management games, the application simulates the campus of the University of Applied Sciences Mittweida, where students are given control of agents acting as archetypal roles of students. The agents' goal is the acquisition of knowledge, an abstract resource gained by participating in courses, requiring the agent to navigate the campus. Students interact with the system through an API that provides information on the state of the simulation and allows issuing commands to specific agents. For example, agents who continuously acquire knowledge over a prolonged period do so at decreasing efficiency. To remedy this, a student implements a routine checking the learning efficiency of all agents, commanding "tired" agents to take a break. Alternatively, the student could train a machine learning algorithm to do the same task, albeit more adaptive. Additionally, the application enables dynamic changes to the environment at runtime, such as adding or removing courses or buildings, simulating player-driven alterations to the game world. By designing decision-making algorithms for these agents, students gain hands-on experience with fundamental AI concepts, i.e. decision trees, bridging the gap between theoretical knowledge and practical application.To evaluate the effectiveness of the application, a comparative study with undergraduate students is planned. Over the course of two semesters, two groups of students will be taught the basics of game AI - one using traditional teaching methods (primarily lectures), the other using a game-based method incorporating the new application. The learning progress of both groups will be monitored using assignments, with students being given a sample project and tasked to develop a game AI solution, i.e. for a non-player character in a first-person shooter.

This research explores the critical role of listening in language learning, highlighting its significance as a foundational skill for second language acquisition. It provides a comprehensive analysis of different types of listening, including discriminative, comprehensive, critical, and appreciative listening, while also examining cognitive processing models such as bottom-up, top-down, and interactive approaches. The study reviews key theories from scholars like Vandergrift, Goh, Rost, and Richards, emphasizing the active and strategic nature of listening. Additionally, recent research on podcastbased instruction, AI-driven tools, and explicit listening strategy teaching is examined. The paper underscores the need for structured listening instruction and technological integration to enhance learner engagement and comprehension. Future research directions include data-driven methodologies and innovative tools to further develop listening proficiency in language education

Artificial Intelligence (AI) and low-code/no-code (LCNC) platforms are reshaping the processes of product development, offering new pathways for efficiency, accessibility, and creativity. By reducing technical barriers, these technologies enable novel approaches to problem-solving and innovation. This research examines the design and implementation of a university-level course structured around the CDIO (Conceive, Design, Implement, Operate) framework, leveraging AI-powered LCNC platforms. The course introduces foundational software development skills, including system design, UI/UX principles, and the creation of innovative solutions for everyday problems in a practical and accessible manner. As a project-based course, it emphasizes interpersonal and strategic skills, fostering personal and team productivity, as well as small-scale project management using an adapted agile approach. By integrating AI-driven tools into the curriculum, the course bridges human creativity and technological capabilities, enabling students to rapidly ideate, design, and deliver concrete solutions with a user-centered mindset. This study reflects on the outcomes of the first implementation of the course from both educator and student perspectives. It focuses on the course’s impact on collaboration, creativity, and problem-solving in small-scale software development projects, while also considering whether AI-assisted LCNC platforms provide a more accessible entry point into programming. The findings lay the groundwork for future research, including a planned AI-assisted programming course, which will further investigate how these tools can make programming more approachable for beginners. From the students’ perspective, the course highlighted the potential of AI-powered tools to reduce cognitive load, enable rapid prototyping, and foster multidisciplinary teamwork. However, challenges arose with the restricted functionality of free versions, particularly when students attempted to scale their projects or access advanced features. Despite these limitations, many students reported increased confidence in their ability to contribute to technology-driven projects, even with limited prior technical experience. Instructors observed that the course significantly accelerated the development process, enabling students to focus more on user-centric design and strategic decision-making. However, they emphasized the need to address tool limitations through careful project scoping and creative solutions. Ethical discussions on AI usage, data privacy, and societal impacts were particularly impactful, encouraging students to critically reflect on the broader implications of their work and the practical realities of deploying such tools in professional contexts. The course also underscored the importance of equitable access to resources and a deeper understanding of licensing models for LCNC platforms. While the free versions lowered barriers to entry, they presented challenges in replicating the functionality required for real-world project deployment. Balancing the course’s educational goals with these constraints became a central consideration, preparing students to navigate similar challenges in professional environments. In conclusion, the initial implementation of the course demonstrated the potential of AI-powered LCNC platforms to enhance human factors in product development by streamlining workflows and fostering collaboration. At the same time, it highlighted the importance of adaptive teaching practices to meet diverse student needs and prepare them for practical challenges. These findings provide a foundation for refining future iterations of the course.

The field of User Experience (UX) has evolved rapidly, driven by technological advances, the normalization of Agile methodologies in software design, and the rise of AI. Despite this growth, UX education struggles to keep pace, often prioritizing static concepts or specific tools over the adaptable skillsets required in today’s industry. The gap between classroom learning and real-world application leaves graduates ill-equipped to navigate a dynamic landscape where expectations for UX professionals are broader and more integrated than ever. This paper critiques the current state of UX education and presents a framework to align academic curricula with industry needs.Central to this critique is the understanding that UX should not exist as a siloed field. UX is not visual design, nor is it solely research. It is a process that encompasses problem-solving, iterative learning, and collaboration. To succeed, UX professionals must work as part of a product team, integrating their efforts with developers and other stakeholders to create solutions that reduce costs and deliver value. Yet, many UX programs fail to teach students how products are built from end to end, focusing instead on artifacts like wireframes and mockups rather than the broader process. Classrooms frequently neglect critical skills such as coding, meeting or workshop facilitation, and project ownership, which are essential for effective collaboration with stakeholders and cross-functional teams.The industry’s cyclical need for specialization versus generalization underscores the importance of adaptability. Over-specialization in areas like interaction design or visual design can limit career growth, especially when technological priorities shift. Instead, students should learn the core UX process and apply it across diverse contexts. This adaptability extends to tooling. Tools like Figma are constantly evolving, requiring educators to teach principles rather than platforms to ensure students understand concepts that transcend specific software. The analogy to painting illustrates this approach, where techniques like shading remain consistent regardless of medium (watercolor, acrylic, oil). Mastering tools is not synonymous with mastering UX, or any discipline.To address these gaps, educators must rethink how they prepare students for careers in UX. Programs should emphasize hybrid skills that integrate research, visual communication, and technical understanding. Students must engage in all parts of the product process, learning how to prioritize features with technical leads and collaboratively plan solutions. Education should also foster teamwork and open communication, teaching students how to work asynchronously and synchronously with peers across disciplines. Effective soft skills, such as giving and receiving critique and presenting ideas clearly, are critical for professional success.In addition to foundational changes, UX programs must embrace continuous improvement. Curricula should be updated yearly based on summer research into emerging tools, processes, and best practices. Faculty should cross-train during breaks, exploring industry trends and enhancing their skillsets. This iterative approach mirrors the reality of UX work, where learning and pivoting are constant.Ultimately, the success of UX education lies in preparing students for a rapidly evolving field. By integrating real-world practices, fostering flexibility, and embedding UX within broader product development processes, educators can empower graduates to thrive. This paper challenges institutions to rethink their approach and offers practical strategies to bridge the gap between academic theory and industry expectations.