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This course addresses the use of artificial intelligence to create computer-based intelligent tutoring systems.
This course addresses the use of Artificial Intelligence to create computer-based Intelligent Tutoring Systems (ITSs). Students will learn theoretical and data-driven methods for creating ITSs. ITSs have been demonstrated to dramatically enhance student learning in many domains, including, to name a few, mathematics, computer science, medicine, biology and engineering. In addition to discussion and readings about methods and models of problem solving, learning, and tutor design, the course will have a "learning by doing" component.
1. Critique key concepts in artificial intelligence in education and the psychology of learning [WA2, WA10]2. Critically assess approaches to student modelling in ITSs [WA2]3. Develop constraints and production rules for use in ITSs [WA3]4. Develop small-scale constraint-based tutors [WA3]5. Critically analyse current research topics in the area of Artificial Intelligence in Education [WA10, WA12]
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Subject to approval of the Head of Department.
Students must attend one activity from each section.
Tanja Mitrovic
2024
Please click HERE for the CSSE Department's policy for the academic remedy of applications for a special consideration for final exams.
Domestic fee $1,176.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Computer Science and Software Engineering .