Leveraging Artificial Intelligence to Enhance learning modes

• In Event: The role of generative artificial intelligence (AI) in Education: inclusion, innovation, and impact

Proposal

With the emergence of artificial intelligence (AI), new opportunities arise to enrich and personalize learning methods. AI's ability to analyze large-scale learning data allows for personalized recommendations and real-time feedback for students. For instance, intelligent tutoring systems can adapt practice exercises based on individual performance, thereby optimizing the acquisition and practice process. Furthermore, educational chatbots and virtual assistants facilitate discussion and reflection by providing instant responses and encouraging continuous interaction.

Generative AI in education represents a transformative leap forward, leveraging advanced machine learning algorithms to create personalized and adaptive learning experiences. Unlike traditional educational technologies, generative AI can produce new content, such as customized exercises, assessments, and interactive simulations tailored to individual learner needs. This technology not only responds to students' current performance but also anticipates their future learning paths, providing targeted support and resources to bridge knowledge gaps. Moreover, generative AI facilitates the creation of virtual tutors and mentors, capable of engaging in complex dialogues and offering nuanced feedback that mimics human interaction. By harnessing these capabilities, generative AI holds the promise of revolutionizing educational environments, making learning more efficient, engaging, and accessible to a diverse range of students.

The primary aim of this study is to juxtapose Diana Laurillard’s (2012) "Conversational Framework" with the innovative capabilities presented by AI. Laurillard’s framework identifies six key learning activities: acquisition, practice, investigation, production, discussion, and collaboration. This study investigates how AI can transform these learning modalities and bolster the pedagogical alignment suggested by Lebrun. By exploring AI’s potential to enhance each learning mode, this research seeks to determine the efficacy of AI in aligning learning activities with specific educational goals and assessment criteria.

To achieve the study's objective, we conducted a comprehensive literature review and analysis of existing AI applications in educational contexts. This involved examining case studies and empirical research that demonstrate the integration of AI into various learning modes. Our findings indicate that AI significantly enhances each of Laurillard’s learning modes by providing a scalable and practical implementation of the Conversational Framework. Key observations include:

· Acquisition : AI-driven content delivery systems, such as adaptive learning platforms like Knewton or Smart Sparrow, personalize educational content based on individual learner profiles. These systems analyze a student's learning pace, preferences, and performance history to curate customized lessons and materials.

· Practice : Intelligent tutoring systems (ITS), offer real-time adaptation of practice exercises based on student performance. When a student makes a mistake, the system immediately adjusts the difficulty level of subsequent questions, offers hints, and provides step-by-step solutions to help the student understand the concept. This dynamic adjustment ensures that practice opportunities are always at the optimal challenge level, neither too easy nor too difficult, which maximizes learning efficiency and retention.

· Investigation : AI tools provide personalized inquiry-based learning paths. These systems enable students to delve deeper into topics by guiding their exploration with tailored suggestions and resources. The AI could also suggest related topics and questions to further stimulate the student's curiosity and critical thinking skills.

· Production : Generative AI can assist students in creating their own content by offering real-time feedback and improvement suggestions. For example, some AI tools analyze student writing for grammar, coherence, and originality, providing instant feedback on how to enhance their work. In creative fields, AI platforms can help students produce engaging multimedia presentations by suggesting relevant images, videos, and music, thereby fostering creativity and improving the quality of their projects.

· Discussion : Educational chatbots, facilitate ongoing dialogue and reflection among students. These chatbots can answer questions, provide explanations, and encourage students to think more deeply about the material. This continuous interaction helps solidify understanding and promotes a deeper engagement with the subject matter.

· Collaboration : AI-powered platforms, enhanced with AI capabilities, improve collaborative learning by matching students with complementary skills and knowledge for group projects. These platforms can analyze individual strengths and weaknesses, suggesting optimal team compositions to ensure a balanced skill set. This targeted collaboration fosters a more effective and enriching group learning experience.

The study concludes that AI provides the technological foundation necessary to effectively implement Laurillard’s Conversational Framework on a practical and scalable level. By optimizing each mode of learning and ensuring alignment with educational objectives and assessments, AI enables a highly personalized and effective learning experience. The integration of AI in education represents a significant advancement in the ability to cater to the diverse needs of learners, ultimately leading to improved educational outcomes.

Authors

• Mourad BENALI

• Janice Mak, Arizona State University