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Chatbots in Education - Coggle Diagram

- - - - (Pérez et al. 2020) Teaching-oriented chatbots AC2
      - (Pérez et al. 2020) Service-oriented chatbots AC1
    - - (Winkler, Söllner 2018) Language learning chatbots AC4
      - (Winkler, Söllner 2018) Feedback and metacognitive thinking chatbots AC5
      - (Winkler, Söllner 2018) Motivation and self-efficacy chatbots AC6
      - (Winkler, Söllner 2018) Health and well-being interventions chatbots AC3
  - - - English 75-89%
        (Pérez-Marín 2021),(Smutny, Schreiberova 2020) AS3.1
    - - (Smutny, Schreiberova 2020) ~18% language learning AS2.1
      - (Hobert, Wolff 2019) ~66% focus on a single domain only AS2.2
    - - (Smutny, Schreiberova 2020) ~47% informing
        AS1.1
      - (Hobert, Wolff 2019) ~25% formal learning
        AS1.3
      - (Pérez-Marín 2021) ~42% mixed interaction modalities AS1.2
      - (Hobert, Wolff 2019) Majority does not enable
        student-student communication AS1.4
  - - - Stong relation to Skill Improvement
        and Students' Motivation
    - - Strong Relation to Efficiency of Education
    - - Mentoring Topic
        
        Selff-Regulated Learning
        
        Life Skills
        
        Learning Skills
      - Mentoring Methods
        
        Scaffolding
        
        Recommending
        
        Informing
      - Equally related to all reasons for chatbots
- - - - DC2.2 Ability to incorporate context data
        (Winkler, Söllner 2018)
      - DC2.3 Speech or text interface
        (Winkler, Söllner 2018)
        
        Text Interface
        
        More suitale for conveying information
        (Jung et al. 2020)
        
        Button-based (Smutny, Schreiberova 2020)
        
        Keyword-based (Smutny, Schreiberova 2020)
        
        Speech Interface
        
        More suitable for affective support (Jung et al. 2020)
      - DC2.1 Retrieval vs. generative technology
        (Winkler, Söllner 2018)
    - - DC1.2 Classification Framework 2
        (Pérez-Marín 2021)
        
        Pedagogy
        
        Interaction type
        
        Domain
        
        Role and attitude
        
        Social
        
        Support
        
        Empathy
        
        HCI
        
        Virtual character
        
        Adaptivity
        
        Ubiquity
        
        Type of animation
        
        Language
        
        Student features
      - DC1.1 Classification Framework 1
        (Winkler, Söllner 2018)
        
        artificially intelligent chatbots
        
        flow chatbots
        
        chatbots with integrated speech recognition
        
        chatbots with integrated context-data
  - - - PS1 Positive or neutral emotional expressions
      - PS2 Limited amount of animated or visual graphics
      - PS3 Well-considered gender
      - PS4 Human-like interactions
  - - - AD1.2 feedback adaptation
      - AD1.1 learning material selection
- - - - Embeddeding in learning scenario EC1.1
        (Winkler, Söllner 2018), (Smutny, Schreiberova 2020)
      - Teaching efficiency EC1.2
        (Pérez et al. 2020)
    - - Acceptance and adoption EC2.1
        (Hobert 2019)
      - Students' perception EC2.2
        (Pérez et al. 2020)
- - - - Attitude and trust towards technology
      - Learning characteristics
      - Educational background
      - Social skills
      - Technological skills
      - Self-efficacy
      - Self-regulation skills