Annie is a system for embedding intelligent tutors into digital games. Straightforward addition of educational content to games has often been ineffective. Critics charge that adding overt educational content to gamesmerely “sucks the fun out”, while others protest that adding game elements to established learning technologies “sucks out the learning” diluting learning efficiency. Thoughtful simultaneous design of learning and gameplay can lead to success, but this often comes at the expense of coupling game and tutorial logic, limiting the scalability and reusability of the system. Our approach uses the a single plan-based representation as the basis for both the core mechanics and intelligent tutoring to be embedded in the game. Our goal is to facilitate the construction of a new class of educational experiences through a tight integration of game mechanics and learning, driven by general, reusable tutorial plug-ins.
The name “Annie” was chosen in recognition of Anne Sullivan, who used innovative and imaginative tutoring to guide the blind and deaf Helen Keller in learning to communicate through words. ·An open exploratory learning environmen poses challenges that echo those faced by Anne in dealing with Helen’s restricted communicative facilities and high degree of autonomy. Like Anne Sullivan, Annie must intelligently cope with a highly uncertain model of the student’s understanding while continuing to gently guide the
student through trial and error learning.
Thomas, James and Young, R. Michael, Annie: Automated Generation of Adaptive Learner Guidance For Fun Serious Games, to appear in the IEEE Transactions on Learning Technologies special issue on Learning in Games.
Thomas, James and Young, R. Michael, Annie: A Tutor that Works in Digital Games, to appear in the Proceedings of the Tenth International Conference on Intelligent Tutoring Systems, Pittsburgh, PA, 2010. [PDF available soon]
Thomas, James M. and Young, R. Michael. Guiding discovery learning with an extensible representation of actions in digital games. Technical Report DGRC-2009-01, Digital Games Research Center, North Carolina State University, Raleigh, North Carolina, 2009.
Thomas, James and Young, R. Michael, Using Task-Based Modeling to Generate Scaffolding in Narrative-Guided Exploratory Learning Environments, in the Proceedings of the International Conference on Artificial Intelligence and Education (AIED 09), Brighton, UK, July, 2009. [PDF]
Thomas, James and Young, R. Michael, Dynamic Guidance in Digital Games: Using an Extensible Plan-Based Representation of Exploratory Games to Model Student Knowledge and Guide Discovery Learning, in the Working Notes of the Intelligent Educational Games Workshop at the International Conference on Artificial Intelligence and Education (AIED 09), Brighton, UK, July, 2009. [PDF]
Thomas, Jim and Young, R. Michael, A Domain-Independent Framework to Automate Scaffolding of Task-Based Learning in Digital Games, in the Proceedings of the International Conference on the Foundations of Digital Games (ICFDG 09), Orlando, FL, April 26 – 30, 2009. [PDF]
Annie video or image files
Annie in the news
Please contact R. Michael Young (young at csc.ncsu.edu)
The US National Science Foundation, through CAREER Award #0092586 and Gradaute Research Fellowship Award #05xxxxx.