Lära

Lära erbjuder tre dialogtyper för att stödja och strukturera ditt lärande: Upptäckt, Problemlösning och Fokuserat lärande. Du styr samtalets riktning, takt och djup. Lära anpassar språknivå och förklaringar efter dina behov. Fråga hur du kan styra samtalet och prova att prata med Lära i mobilen!

Lära består av två delar en instruktion till AI:n och en till eleven/studenten för att styra samtalet med AI.

Background: Lära - Concept for AI-Assisted Learning - BETA version!!!!

I. Introduction to Lära

Lära is a concept for leveraging existing Generative AI technologies to facilitate dialogue-based learning experiences. It proposes a framework for engaging learners in meaningful, adaptive conversations that promote deep understanding and metacognitive skill development, utilizing the capabilities of current AI language models. Central to Lära's design is the empowerment of learners to guide their own learning process, making decisions about the direction and nature of their interactions with AI.

II. Theoretical Foundations

Lära's conceptual design is grounded in several key educational and cognitive theories:

1. Dialogic Theory of Learning (Wegerif, 2007)

   • Conceptualizes learning as occurring within a "dialogic space" created through interaction
   • Emphasizes how AI can expand and enrich this dialogic space, fostering critical thinking and deeper understanding

2. Socio-cultural Theory of Learning (Lantolf & Thorne, 2006)

   • Extends Vygotsky's work to include technology-mediated learning environments
   • Positions Generative AI as a cultural tool mediating learning processes

3. Guided Participation (Rogoff, 1990)

   • Describes how learners develop through participation in cultural activities
   • Informs how AI-assisted dialogues can guide learners, offering insights into the scaffolding process

4. Cognitive Apprenticeship (Collins, Brown & Holum, 1991)

   • Influences Lära's approach to using AI for modeling expert thinking and problem-solving strategies
   • Guides the implementation of fading support to promote learner autonomy

5. Self-Regulated Learning (Zimmerman, 2002)

   • Shapes strategies for developing learners' abilities to plan, monitor, and evaluate their own learning through AI-assisted dialogues

6. Cognitive Load Theory (Sweller, 1988)

   • Informs the design of interactions to optimize cognitive processing
   • Guides the implementation of strategies to manage intrinsic, extraneous, and germane cognitive load

7. Schema Theory (Piaget, 1952; Bartlett, 1932)

   • Influences the approach to organizing and presenting information to facilitate the construction and modification of mental schemas
   • Guides the design of learning experiences that build upon and challenge existing knowledge structures

8. Information Processing Theory (Miller, 1956; Atkinson & Shiffrin, 1968)

   • Informs the structuring of information presentation and retrieval processes
   • Guides the implementation of strategies to support effective encoding, storage, and retrieval of information

III. Key Features of Lära

1. Learner Agency and Adaptive Dialogic Interaction

   • Empowers learners to initiate and guide their learning journey, choosing the types of dialogues they engage in
   • Proposes methods for engaging learners in personalized, context-aware conversations using Generative AI
   • Suggests ways to adjust language complexity and content depth based on learner preferences and responses
   • Outlines implementation of dynamic scaffolding through dialogue, with learners deciding when to seek more or less support

2. Multiple Dialogue Types

   • Allows learners to select and switch between dialogue types based on their needs:
     • Information-Seeking and Broad Picture Dialogue
     • Conceptual Learning and Cognitive Processing Dialogue
     • Clarification and Diagnostic Dialogue
     • Problem-Solving Dialogue
     • Explanatory and Reflective Dialogue
     • Task-Focused Follow-Up Dialogue
     • Confusion-Resolution Dialogue
     • Exploratory Dialogue
     • Consolidation Dialogue
     • Argumentative Dialogue

3. Metacognitive Development

   • Describes prompts for reflection on learning processes, with learners choosing when to engage in metacognitive activities
   • Encourages self-explanation and elaboration through AI-guided conversations
   • Supports the development of self-regulation skills, allowing learners to monitor and adjust their learning strategies

4. Cognitive Load Management

   • Proposes structures for interactions to optimize cognitive processing, with learners controlling the pace and depth
   • Suggests methods for breaking down complex topics into manageable chunks, allowing learners to request simplification or elaboration as needed
   • Outlines provision of just-in-time support to prevent cognitive overload, activated at the learner's discretion

5. Integration with Existing Educational Structures

   • Acknowledges the availability of curricula, learning objectives, and course content (e.g., textbooks) within the Lära concept
   • Proposes flexible integration with existing educational frameworks, allowing learners to align AI interactions with their formal learning goals

6. Language Adaptation and Multilingual Support

   • Empowers learners to adjust the complexity of language used by the AI, from simplified explanations to more scientific or technical language
   • Allows learners to request translations of specific concepts or switch the entire dialogue to a different language
   • Supports multilingual learning by providing explanations of concepts in multiple languages, enhancing understanding and broadening linguistic competence

7. Culturally Responsive Learning

   • Proposes ways to adapt dialogue strategies to different cultural learning styles
   • Suggests methods for promoting cross-cultural understanding through AI interactions

8. Cognitive Theory-Based Learning Design

• Applies Schema Theory principles to facilitate the construction and modification of mental schemas
• Structures information presentation and retrieval processes based on Information Processing Theory
• Implements strategies to activate prior knowledge and build upon existing cognitive structures
• Utilizes techniques like chunking, multimodal presentation, and spaced repetition to enhance encoding, storage, and retrieval of information
• Adapts the complexity and presentation of content to align with learners' cognitive processing capabilities and existing knowledge structures

IV. Unique Aspects of AI-Human Learning Dialogue

Lära's approach to dialogue differs from traditional human-human interaction in several key ways:

1. Turn-taking Dynamics

   • Utilizes AI's capability for immediate responses and handling of extended, complex turns
   • Leverages AI's persistent attention and recall of previous interactions
   • Proposes flexible dialogue structures and pacing, controlled by the learner

2. Prompt Engineering and Context Management

   • Educates learners on effective prompt formulation to guide AI responses
   • Utilizes AI's context windows to maintain coherent, multi-turn conversations
   • Allows learners to actively manage and direct the context of their learning dialogues

3. Multimodal Integration

   • Explores potential for incorporating various input types (text, voice, images) into the dialogue, as chosen by the learner
   • Suggests seamless integration of multimedia resources to support learning, with learners selecting preferred modes of interaction

4. Personalization at Scale

   • Proposes methods to tailor dialogues to individual learner profiles and learning objectives
   • Leverages AI's consistent availability for extended, in-depth interactions

5. Scaffolding and Fading

   • Suggests methods for dynamic adjustment of support based on real-time assessment of learner needs
   • Proposes gradual transfer of responsibility to the learner as competence increases

V. Cognitive Learning Strategies

Lära incorporates various cognitive learning strategies to enhance the learning process:

1. Chunking

   • Breaks down complex information into manageable "chunks" to facilitate easier processing and retention
   • Aligns with cognitive load theory and information processing theory

2. Elaboration

   • Encourages learners to expand on concepts, relating new information to existing knowledge
   • Supports the construction and refinement of mental schemas

3. Retrieval Practice

   • Implements strategies for active recall of information to strengthen memory and understanding
   • Aligns with research on the testing effect and spaced repetition

4. Dual Coding

   • Utilizes both verbal and visual representations of information to enhance comprehension and retention
   • Draws on Paivio's Dual Coding Theory (1971)

5. Metacognitive Strategies

   • Promotes planning, monitoring, and evaluation of one's own learning processes
   • Supports the development of self-regulated learning skills

VI. Recent Research on Generative AI and Dialogic Learning

Recent studies have explored the potential of generative AI in dialogic learning approaches:

1. AI-Enhanced Collaborative Learning (Chiu, 2023)

   • Investigates how AI can facilitate more effective peer-to-peer learning dialogues
   • Highlights the potential for AI to scaffold collaborative knowledge construction

2. Adaptive Scaffolding in AI Tutoring Systems (Zhang et al., 2022)

   • Examines the effectiveness of AI-driven adaptive scaffolding in one-on-one tutoring contexts
   • Demonstrates improved learning outcomes through personalized support

3. Natural Language Processing for Educational Dialogue Analysis (Luan & Wang, 2021)

   • Explores the use of NLP techniques to analyze and enhance educational dialogues
   • Offers insights into automated assessment of dialogue quality and learning progress

4. Ethical Considerations in AI-Mediated Learning Dialogues (Morales-Gamboa et al., 2023)

   • Addresses ethical challenges in implementing AI-driven dialogic learning
   • Proposes frameworks for ensuring fairness, transparency, and privacy in AI-learner interactions

5. Cross-Cultural Adaptivity in AI Learning Assistants (Tanaka et al., 2022)

   • Investigates the potential for AI to adapt its dialogic approach to different cultural contexts
   • Highlights the importance of cultural sensitivity in global AI-enhanced learning environments

VII. Challenges and Considerations

While Lära offers significant potential for enhancing learning experiences, several challenges must be addressed:

1. Balancing AI Guidance and Learner Autonomy

   • Ensuring the concept supports rather than replaces independent thinking
   • Developing strategies to gradually reduce reliance on AI assistance, with learners actively participating in this process

2. Cognitive Considerations

   • Addressing the metacognitive load of constantly evaluating AI responses
   • Developing learners' skills in abstraction and generalization when interacting with AI

3. Adapting to AI Limitations

   • Acknowledging current limitations of Generative AI and educating learners on these constraints
   • Encouraging learners to critically evaluate AI responses and seek verification when necessary

4. Ethical and Privacy Concerns

   • Ensuring the protection of learner data and privacy
   • Addressing potential biases in AI responses and ensuring equitable access to AI-enhanced learning

5. Integration with Traditional Educational Practices

   • Developing strategies for seamless integration of Lära with existing educational structures and pedagogies
   • Providing support for educators in effectively utilizing AI-enhanced learning tools

Conclusion

Lära, as a concept for utilizing Generative AI in learning contexts, represents a significant step forward in AI-assisted education. It aims to create a learning environment that is not only informative but also engaging, adaptive, and supportive of higher-order thinking skills. By leveraging existing AI technologies and empowering learners to take control of their educational journey, Lära has the potential to revolutionize personalized learning experiences. However, careful consideration of the challenges and ongoing research will be crucial in realizing its full potential and ensuring its effective and ethical implementation.

Further reading

1. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89-195). Academic Press.

2. Bakhtin, M. M. (1981). The dialogic imagination: Four essays. University of Texas Press.

3. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

4. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.

5. Bruner, J. S. (1966). Toward a theory of instruction. Harvard University Press.

6. Chiu, T. K. F. (2023). Applying artificial intelligence in collaborative learning: A systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), 1-24.

7. Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator, 15(3), 6-11.

8. Lantolf, J. P., & Thorne, S. L. (2006). Sociocultural theory and the genesis of second language development. Oxford University Press.

9. Luan, H., & Wang, M. (2021). Natural language processing for educational dialogue analysis: A systematic literature review. Educational Research Review, 34, 100398.

10. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.

11. Morales-Gamboa, R., Restrepo-Calle, F., & Segovia-Chaves, F. (2023). Ethical considerations for AI-mediated learning dialogues: A framework for designers and educators. AI and Ethics, 3(2), 411-425.

12. Paivio, A. (1971). Imagery and verbal processes. Holt, Rinehart and Winston.

13. Piaget, J. (1952). The origins of intelligence in children. International Universities Press.

14. Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. Oxford University Press.

15. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.

16. Tanaka, Y., Lee, J., & Kato, S. (2022). Cross-cultural adaptivity in AI learning assistants: Challenges and opportunities. Journal of International Education in Business, 15(2), 182-199.

17. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

18. Wegerif, R. (2007). Dialogic education and technology: Expanding the space of learning. Springer Science & Business Media.

19. Zhang, L., Huang, X., & Liu, T. (2022). Adaptive scaffolding in AI tutoring systems: A meta-analysis of effectiveness across domains. Computers & Education, 179, 104468.

20. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64-70.