LearnLab
Core Course

Adaptive Learning and Intelligent Tutoring Systems

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Beginner level

No prior experience required

Flexible schedule

4 weeks, 6 to 8 hours per week

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*Proof of full-time student enrollment required. Acceptable forms of ID include a letter from your university’s registrar office or an unofficial transcript. Email your documents to learnlab-help@lists.andrew.cmu.edu.

What you will learn

  • Explain the main goals and components of adaptive learning and intelligent tutoring systems.
  • Compare different sources of learner information used to drive personalization.
  • Design adaptive supports based on learner knowledge, behavior, and affect.
  • Evaluate personalization strategies for both instructional value and practical feasibility.

Course description

Adaptive learning systems aim to tailor instruction to individual learners, but meaningful personalization depends on understanding what can be adapted, what evidence is needed, and how support should change as learners progress. The best systems go beyond branching paths to model what learners know, how they behave, and where they need help.

In this course, you will examine the foundations of adaptive learning and intelligent tutoring systems, including personalization by knowledge, problem path, and learner state. You will learn how adaptive systems can support more effective instruction, what kinds of learner data matter, and how to design adaptive experiences that are both evidence-based and practically useful.

Syllabus

Module 1: Course Overview
  • Explain national and global educational challenges.
  • Define Intelligent Tutoring System (ITS).
  • Define adaptivity in learning technologies.
  • Describe the adaptivity grid and the relationship between its components.
  • Explain how ITSs adapt to learners.
Module 2: ITS Features and Adapting to Student Data
  • Distinguish between CAI, CBT, or WBH and ITS.
  • Describe the most common instructional design principles for Cognitive Tutors.
  • Define ITS instructional features and design space as described by VanLehn.
  • Explain the common task selection techniques of the outer loop.
  • Describe the design issues in ITS outer and inner loop.
Module 3: Self-Explanation and Help-Seeking
  • Explain self-regulated learning (SRL).
  • Describe the phases and subprocesses of self-regulation.
  • Explain how self-regulated learning could benefit from using ITSs.
  • Describe the impact of supporting self-explanation on learning.
  • Articulate the role of help-seeking in ITS.
Module 4: Affect-Aware Learning Technologies
  • Describe how student’s affect relates to learning.
  • Compare proactive and reactive affect systems.
  • Explain the results and issues of using Affect-Aware Learning Technologies (AALT).
Module 5: Adapting to Learning Styles & Interests
  • Define Learning Styles.
  • Explain key research findings on adapting to learning styles.
  • Explain key research findings on adapting to student interests.
Module 6: Course Project or Final Exam

At the end of the course, you’ll have an opportunity to do a project where you will evaluate and redesign a cognitive tutor using the design principles you learned in the course. It will be graded by the instructor and you will receive personalized feedback along with a sample solution.

You will also have the option to take a final exam with 20 questions. The exam can be taken multiple times, and each attempt draws new questions randomly from a pool of questions.

You may also complete both the course project and the final exam. The higher of the two scores will count toward the certificate.

Meet the instructor

Dr. Vincent Aleven

Dr. Vincent Aleven

Professor
Carnegie Mellon University

Vincent Aleven’s research aims to advance the science of how people interact and learn with adaptive, AI-based learning technologies, and to advance the design and engineering of these technologies. Practically, he aims to help realize the smart classroom through strong synergy among learners, those who facilitate learning such as teachers, instructors, peers, tutors, and parents, and novel AI applications. In this context, he is excited to help a new generation of scientists and professionals develop interest and skill in research and development.
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Dr. Ken Koedinger

Dr. Ken Koedinger

Professor
Carnegie Mellon University

Ken Koedinger is the Hillman University Professor of Computer Science at Carnegie Mellon University, with appointments in Human-Computer Interaction and Psychology. He holds an M.S. in Computer Science and a Ph.D. in Cognitive Psychology and has experience teaching in an urban high school. He has developed data-sharing and analytics infrastructures that support innovations in learning, including DataShop and LearnSphere, and has used them to improve learning as illustrated in his hundreds of publications. He directs LearnLab and co-founded Carnegie Learning in 1998, the first AI in Education company to bring intelligent tutoring technology into widespread use in schools. His PLUS project provides hybrid human-AI tutoring to middle school math students in schools around the country. He is a fellow of the Cognitive Science Society, the Association for Psychological Science, and the Association for Computing Machinery.
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