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Can be handily used in many new domains that lack of learning theory.   -Min Chi

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Can be handily used in many new domains that lack of learning theory.   -Min Chi

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It can handle more learning experience. -Min Chi

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It can handle more learning experience. -Min Chi

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Data-driven can be less expensive. -Min Chi

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Data-driven can be less expensive. -Min Chi

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Data-driven can be more powerful. -Min Chi

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Data-driven can be more powerful. -Min Chi

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Often need data, even more, large data. -Min Chi

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Often need data, even more, large data. -Min Chi

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Noisy data, asynchronized data. -Min Chi

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Noisy data, asynchronized data. -Min Chi

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The disconnection between the learning theory and software Engineering. -Min Chi

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The disconnection between the learning theory and software Engineering. -Min Chi

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Lack of explainability (XAI, XML). -Min Chi

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Lack of explainability (XAI, XML). -Min Chi

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It is often to interpret the learned model or pedagogical policy. -Min Chi

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It is often to interpret the learned model or pedagogical policy. -Min Chi

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New research area for AIED, UM, and EDM. -Min Chi

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New research area for AIED, UM, and EDM. -Min Chi

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New research area for Cognitive modeling, Cognitive Science. -Min Chi

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New research area for Cognitive modeling, Cognitive Science. -Min Chi

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New research area for AI & Machine Learning and Data Mining: 1) Offline -> online, 2) Transfer Learning, 3) Domain adaption. -Min Chi

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New research area for AI & Machine Learning and Data Mining: 1) Offline -> online, 2) Transfer Learning, 3) Domain adaption. -Min Chi

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Determine the effective action choices. -Min Chi

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Determine the effective action choices. -Min Chi

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Ability to adapt to learner and instructor needs. -Gautam Biswas

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Ability to adapt to learner and instructor needs. -Gautam Biswas

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Generalizability of approach. -Gautam Biswas

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Generalizability of approach. -Gautam Biswas

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Ability of system to adjust instruction from one domain to the next. -Gautam Biswas

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Ability of system to adjust instruction from one domain to the next. -Gautam Biswas

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Ability to employ multimodal learning analytics in understanding learner behaviors. -Gautam Biswas

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Ability to employ multimodal learning analytics in understanding learner behaviors. -Gautam Biswas

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Self improvement process may be slow and unreliable. -Gautam Biswas

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Self improvement process may be slow and unreliable. -Gautam Biswas

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Human learners and instructors may find it hard to interpret self-improving behaviors. -Gautam Biswas

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Human learners and instructors may find it hard to interpret self-improving behaviors. -Gautam Biswas

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Ability to incorporate AI and ML techniques to continually improve system's reasoning ability and performance. -Gautam Biswas

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Ability to incorporate AI and ML techniques to continually improve system's reasoning ability and performance. -Gautam Biswas

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More holistic learner modeling. -Gautam Biswas

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More holistic learner modeling. -Gautam Biswas

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Long-term deployments with low maintenance. -Gautam Biswas

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Long-term deployments with low maintenance. -Gautam Biswas

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Deployments before SIS technologies become mature. -Gautam Biswas

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Deployments before SIS technologies become mature. -Gautam Biswas

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Improvement characteristics and behaviors of system are not acceptable to human users. -Gautam Biswas

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Improvement characteristics and behaviors of system are not acceptable to human users. -Gautam Biswas

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Methods developed do not generalize, and are too expensive to develop. -Gautam Biswas

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Methods developed do not generalize, and are too expensive to develop. -Gautam Biswas

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Expanded scope of learner modeling - ability to capture psychomotor, cogntive, strategic, and teamwork processes from multimodal data

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Identify which components should be self-improved

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Identify which components should be self-improved

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Humans are not entirely tabula rasa.  Perhaps adaptive instructional systems aren't either. People start with inborn structures with some components that learn and others not.  Similarly, an AIS can start with theoretical components built in and others learning with experience.  It combines machine learning and theory.  GIFT can help designers identify which components should be self-improving and which not.

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Do you feel that self-improving systems can gain the trust of users even if they are not explainable? (I think we need to distinguish between these ... we trust many systems that we do not understand!)

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One of the challenges we have been having with applying RLs for inducing pedagogical policy has been the amount of data needed.  Do you have thoughts about how much data is needed, what kinds of tasks this works well for the approach, or doesn't work well?

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How general/transferrable are the "rules" that are learned? Are they specific to the students in the training set? The domain? The tutoring system?

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We may learn from each other.

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Three things we may need to consider:
1) Self-Improvable of an ITS: The capability
2) Self-Improvability of ITS; The "measure" of the capacity
3) Self-Improving of ITS: The process that making the capability working in real systems.

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When we first started to discuss the design goals and principles to establish an ITS framework that became GIFT, a lot of the discussion revolved around engineering tradeoffs. Since the military population primarily relies on military members who are SMEs to generate content and sequence courses, we decided a simple authoring system that might be less flexible was best. As GIFT has grown in complexity, it is obvious that we need some level of automation to expand the capabilities of GIFT without increasing the burden to authors... or even reducing the authors' burden.