The goal of the Adaptive Instructional Systems (AIS) Conference, affiliated to the HCI International Conference, is to understand the theory and enhance the state-of-practice for a set of technologies (tools and methods) called adaptive instructional systems. AISs are defined as artificially intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, preferences, and interests of each individual learner or team in the context of domain learning objectives. The interaction between individual learners or teams of learners with AIS technologies is a central theme of this Conference. AISs observe user behaviors to assess progress toward learning objectives and then act on learners and their learning environments (e.g., problem sets or scenario-based simulations) with the goal of optimizing learning, performance, retention and transfer of learning to work environments.
The focus of this Conference on instructional tailoring of learning experiences highlights the importance of accurately modeling learners to accelerate their learning, boost the effectiveness of AIS-based experiences, and to precisely reflect their long-term competence in a variety of domains of instruction. Conference participants examine modeling, interaction design and standards to facilitate research and development of effective and efficient learning using AISs.
AIS Conference participants support the adoption and advancement of products that use artificial intelligence and advanced technologies to help people learn. Stakeholders include AIS product and service providers, instructional designers, instructors, trainers, learning and development organizations, teachers and school districts, learning engineers and scientists, researchers, foundations, and government agencies.
Authors share their expertise in machine-based instruction including aspects of adaptation, augmentation, and interaction design. They share their visions and findings about AIS technologies (e.g. intelligent tutoring systems, intelligent mentors, and personal assistants for learning) and propose standards to improve the portability, extensibility, and interoperability of AIS technologies with each other and other instructional technologies. AIS Conference participants seek to identify standards for authoring, delivery, interaction design, real-time management, and evaluation of AIS technologies supporting domain classifications: cognitive, affective, psychomotor, and group instruction.
The AIS Conference has been largely supported by members of the AIS Community of Practice, a business alliance with the mission to promote the development and adoption of effective AIS solutions. If you are an AIS provider, user, researcher, or developer, we encourage you to engage with HCII AIS Conference participants to learn more about the AIS Community of Practice and its mission by visiting the AIS Community of Practice Twitter page at #AISCoP_Learn or by contacting Bob Sottilare at bob.sottilare@soartech.com
The related topics include, but are not limited to:
- User Interface & Interaction Design (UI/UX)
- Optimizing Adaptive Learning through Superior User Experiences (UXs)
- AIS User Interfaces (UIs) for Enhanced Usability, Learnability, Satisfaction, and Accessibility
- Novel User Interfaces (UIs) for Adaptive Training & Education
- Designing Hyper-personalized Intelligent Tutoring System (ITS) User Interfaces (UIs)
- User-Centered Design Principles for Intelligent Tutoring Systems (ITSs)
- Effective AIS Interaction Design
- Immersive Modalities for Adaptive Instruction
- Brain-Computer Interfaces (BCIs) for Adaptive Learning (emerging tech)
- Learner Modeling & Assessment
- Assessment of Learner and Team States to Support Adaptive Instructional Decisions
- Modeling Learner States, Patterns, and Trends
- Quantitative and Qualitative Measures of Learning during Adaptive Instruction
- Sensors and Data Sources for Learning Assessment during Adaptive Instruction
- Leveraging Multimodal AI for Real-Time Affective and Cognitive State Detection
- Biometric and Neuro-Adaptive Sensors for Continuous Engagement Tracking (emerging tech)
- Pedagogical Foundations & AIS Design
- Instructional Theories Applied to Adaptive Instruction
- Theoretical Frameworks for Adaptive Instruction
- Learning Engineering, Learning Science Principles to Design, Develop, and Improve Adaptive Instruction
- Adaptive Systems & Models for Learning, Education, and Training
- Adaptive Tools and Methods for Individual Learners and Teams
- Authoring Adaptive Instructional Systems (AISs) for Cognitive, Affective, Psychomotor, and Collaborative Tasks
- Generative AI for Automated Curriculum Design and Lesson Planning
- Autonomous Multi-Agent Systems for Hyper-Personalized Learning (emerging tech)
- Advanced AI, Infrastructure & Standards
- Role of Artificial Intelligence in Adaptive Instruction (design, planning, preparation, execution, and review)
- Ethical Use of Artificial Intelligence in Adaptive Learning Experiences
- Conceptual Models and Interoperability Standards for Adaptive Instructional Systems
- Interoperability, Compatibility & Scalability of AIS Technologies
- Adaptive Instructional Processes in the Cloud, in the Fog & at the Edge
- Augmentation Technologies (Tools and Methods) for Adaptive Instruction
- Large Language Models (LLMs) as Real-Time Conversational Tutors
- Mitigating Bias and Ensuring Fairness in Algorithmic Grading and Feedback
- Predictive AI for Early Identification of Learner Attrition and Performance Drops
- Decentralized and Blockchain-Based Architectures for Secure Learner Data Portability (emerging tech)
- Low-Code/No-Code Authoring Platforms for Rapid AIS Deployment (emerging tech)
- Domains of Application & Contexts
- Adaptive Training & Education for STEM (Science, Technology, Engineering, and Mathematics) Education
- Adaptive Training & Education for Medical Diagnoses and Intervention
- Adaptive Training & Education for Psychomotor Tasks in Live, Virtual, Augmented, and Mixed Reality Environments
- Adaptive Training & Education for Professional Development
- AIS Use in Under-Resourced Contexts
- AI-Driven Upskilling and Reskilling for the Evolving Workforce
- Spatial Computing and Metaverse Environments for Shared, Adaptive Team Training (emerging tech)
- Evaluation, Standards & Governance
- Evaluating the Effectiveness of Adaptive Instructional Systems
- Measures of Effectiveness and Efficiency for Adaptive Instructional Methods
- Recommended Practices for Adaptive Instruction
- Continuous, Data-Driven Evaluation Frameworks for Self-Evolving Adaptive Algorithms (emerging tech)
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Program Chair
Robert Sottilare
Accelint AI, USA
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Program Chair
Jessica Schwarz
Fraunhofer FKIE, Germany
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Board Members
- TBA
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