Hare, R. (2024, June), Board 292: General-Purpose Artificial Intelligence Approaches for Intelligent Tutoring  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46868

\bibitem{asee_peer_46868}
  Hare, R. (2024, June), \emph{Board 292: General-Purpose Artificial Intelligence Approaches for Intelligent Tutoring}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  10.18260/1-2--46868

Ryan Hare.  "Board 292: General-Purpose Artificial Intelligence Approaches for Intelligent Tutoring".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/46868 Internet.  04 Apr, 2025

\bibitem{asee_peer_46868}
  Ryan Hare.
  "Board 292: General-Purpose Artificial Intelligence Approaches for Intelligent Tutoring".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/46868 Internet.  04 Apr, 2025

@INPROCEEDINGS{asee_peer_46868
author = "Ryan Hare"
title = "Board 292: General-Purpose Artificial Intelligence Approaches for Intelligent Tutoring"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/46868}
number = {10.18260/1-2--46868}
}

TY  - CPAPER
AB  - As the scope of higher education expands to include a wider range of students, there is an increasing focus on computer software, applications, and systems that can help improve students' educational experiences. This work comprises a yearly update from an ongoing project with the goal of developing a general-purpose educational system. For our proposed system, student data are translated into appropriate student assistance through reinforcement learning agents. With reinforcement learning, the computer-controlled agents can select what assistance to give students based on their performance. The agents can also adjust future behavior depending on the student's response to the provided assistance. The proposed system is also designed to work with any educational game or system, provided that the system records student data and provides a selection of possible student assistance options.

To demonstrate our proposed system, we show results from in-classroom testing with undergraduate students using the proposed system within an educational serious game. We conducted personal interviews with participating students to get detailed feedback on the implementation, which we discuss within this paper. We also show student performance metrics when interacting with the proposed system, demonstrating the system's ability to provide appropriate and useful assistance to students as they interact with the educational serious game.
AU  - Ryan Hare
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Board 292: General-Purpose Artificial Intelligence Approaches for Intelligent Tutoring
UR  - https://peer.asee.org/46868
DO  - 10.18260/1-2--46868
ER  -