Abstract
Artificial intelligence has had a significant impact on education, particularly owing to its ability to personalize students' learning experiences. AI-driven adaptive systems ensure that education aligns with each student's unique learning styles and abilities by analyzing their strengths and weaknesses, adapting content to their pace and specific needs. This encompasses a range of applications, from intelligent tutoring systems that provide real-time guidance to personalized content recommendations. In addition to fostering engagement and motivation, this approach enhances students' comprehension and retention. The learning process of university students is influenced by numerous distractions, educators face the challenge of identifying pedagogical strategies that are both attractive and effective in achieving educational goals. This chapter explores the possibilities of AI in higher education and how it can enhance the creation of audiovisual materials, mentoring, and information dissemination for students and administrative staff.
TopIntroduction
Education is undergoing a profound paradigm shift as the Fourth Industrial Revolution unfolds. Artificial Intelligence (AI) has played a pivotal role in transforming traditional models into personalized, data-driven, and adaptive approaches. The integration of AI has become a central driver for innovation, promising to reshape pedagogical approaches, administrative functions, student outcomes, and educational inclusivity. Furthermore, AI tools benefit not only students but also educators. The administrative workload of teachers has been reduced thanks to Natural Language Processing (NLP) algorithms, which assist in grading assignments and essays, as well as providing immediate feedback to students. Classroom management is streamlined through virtual platforms equipped with AI-driven chatbots that respond to student queries. This allows instructors to focus more on instruction and less on logistics, enabling educators to create a more user-friendly, enriched, and efficient teaching environment.
Artificial intelligence plays an increasingly significant role in assessing and evaluating student performance. Natural Language Processing (NLP) and machine learning have driven automatic essay scoring systems, ensuring consistent and efficient grading while providing students with immediate feedback on their performance. In standardized testing, AI-driven assessment tools provide more precise and comprehensive evaluations, adapting to individual skill levels, thus offering more accurate and comprehensive assessments. These advancements aim to create fairer and more reliable assessments to improve educational outcomes.
AI has the potential to bridge gaps in education by addressing issues of inclusivity and accessibility. Speech recognition technologies enable transcription services for students with hearing impairments, while AI-driven language translation tools break down language barriers, facilitating global collaboration and understanding. AI-guided interventions can identify and support students with learning disabilities, making education more inclusive and equitable. In the context of Animation and Visual Effects Engineering, AI is employed in the subject “Art History and Animation.” AI structures topics and engages students through virtual tours and virtual reality (VR) resources, creating meaningful learning experiences by connecting prior knowledge with new information.
This chapter aims to demonstrate the utilization of tools provided by new technologies to facilitate meaningful learning, anchoring experiences to prior knowledge and integrating them into cognitive interaction, fostering sustained interest among students. AI systems are also utilized to create customized tutors for class-related inquiries, streamline administrative tasks, generate assessment tools, and provide personalized mentoring to students.
Key Terms in this Chapter
Artificial Intelligence (AI): A scientific discipline that creates computer programs that perform operations comparable to those carried out by the human mind, such as learning or logical reasoning.
Machine Learning (ML): A subfield of Artificial Intelligence that involves the development of systems with autonomous learning. Machine learning implies that computers accumulate experience and improve their performance using data.
Meaningful Learning: The learning process in which new knowledge is linked to the individual's existing knowledge, giving the new learning significance.
Artificial Intelligence for Education (AIED): The application of artificial intelligence computer systems to optimize the teaching-learning process.
Deep Learning: A set of machine learning algorithms that attempt to model high-level abstractions in data using computational architectures that support multiple and iterative non-linear transformations of data expressed in matrix or tensor form.
Natural Language Processing (NLP): A field within computational disciplines that focuses on the interactions between computers and human natural language. It aims to enhance communication between people and machines.
Virtual Reality (VR): A simulated digital environment created by a computer that gives the user the sensation of being immersed in it using peripheral devices such as VR glasses or headsets.
Augmented Reality: The combination of technologies that, through peripheral devices, allows the visualization of graphic elements using technology. These intangible, digital elements can be 2D graphics, 3D models, texts, and the user can interact with them. Augmented reality elements enable incorporating these virtual elements into a real environment.
Generative Artificial Intelligence: A type of Artificial Intelligence system that generates text, multimedia images, and other visual files such as videos and 3D models based on a prompt.