The use of Artificial Intelligence (AI) in Higher Education is emerging as a catalyst for active learning, motivation, and personalized education, allowing content and learning pace to be adapted to each student’s progress while optimizing independent study and virtual tutoring. Among its advantages, AI facilitates the generation of interactive and personalized learning resources, reinforcement activities, and simulations that complement instructors' explanations, supporting concept assimilation and the development of digital competencies. Chatbots and virtual assistants, such as ChatGPT, Gemini or Copilot, provide instant responses, clarify doubts, and offer step-by-step examples, acting as tutors accessible anytime and anywhere. This ongoing interaction fosters student autonomy, promotes self-directed learning, and enables immediate feedback practices that strengthen formative assessment. Furthermore, AI supports the analysis of learning data, enabling educators to identify learning patterns and personalize feedback, thus enhancing guidance in learning processes. However, its implementation requires critical attention due to limitations such as the lack of contextual understanding in complex responses, the potential risk of algorithmic bias, and the need for continuous supervision to ensure information quality. Over-reliance on these tools may replace reflective processes essential for deep learning if not used with appropriate pedagogical guidance. Therefore, while AI offers significant opportunities to enrich educational experiences, it should not replace human interaction, pedagogical judgment, or personalized support that define the essence of teaching.

Integrating Artificial Intelligence with active learning methodologies is essential to fully leverage its transformative potential while maintaining pedagogical integrity. AI-powered tools can complement Flipped Teaching, Project-based Learning, Peer-Instruction, and Game-based Learning by providing adaptive resources, immediate feedback, and personalized pathways that align with each student's progress. For example, in a Flipped model, students can use AI chatbots to clarify concepts before in-person discussions, enabling richer debates and deeper understanding during class sessions. Similarly, AI-assisted simulations in engineering education can allow students to explore complex systems and receive real-time feedback, fostering critical thinking and problem-solving skills. By combining AI with active learning, educators can maintain the focus on student-centered learning while ensuring that technology remains a facilitator, not a substitute, for meaningful educational experiences. This integration promotes a reflective, engaged, and personalized learning environment.

This study, conducted within the Master's in Industrial Engineering, has demonstrated that generative and conversational AI tools can be effectively integrated into Higher Education environments. Virtual assistants have proven versatile and adaptable, providing personalized support while enhancing student motivation, participation, and engagement. AI-based activities have reinforced digital competencies among students, aligning with the needs of modern engineering education. Preliminary results indicate a 30% increase in student participation rates during AI-assisted sessions. Notably, the learning curve for these tools has been quick, allowing seamless incorporation into academic routines.

Keywords: Artificial Intelligence AI, Chatbots, Virtual assistant, Generative and Conversational AI tools, Flipped-Teaching, AI-based activities, Blended Learning, Game-based Learning and Gamification, New experiences in education, New Trends and Experiences, Learning and Teaching Innovations, Higher Education.