Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning

Goslen, Alex; Carpenter, Dan; Rowe, Jonathan P.; Henderson, Nathan; Azevedo, Roger; Lester, James L.

doi:10.1007/978-3-031-11644-5_7

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…For example, based on the information the student has received, such as clues or character dialogue, the AI can predict the student's goals and adapt the plot if needed (Dever et al, 2021). This means students can self regulate their learning with accessible adaptive support when needed (Goslen et al, 2022).…”

Section: Game-based Learningmentioning

confidence: 99%

Exploring the role of AI in education

Nguyen

2023

LJSS

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New advancements in machine learning and AI can be used to augment student learning and teacher capabilities. Examples of AI approaches in education include generating personalized student recommendations, autograding essays, and improving educational resources. AI programs intended to improve education can be categorized informally into three groups: Guidance, Learning, and Teacher. These categories are general and not necessarily mutually exclusive, but provide a framework for organization and further development. This paper intends to look at the past approaches of AI to improve education and categorize them to help guide new development of AI applications in education. The potential benefits of AI-powered education is noteworthy as the current economy is based on higher education. AI can be used to speed up labor-intensive tasks and help close the knowledge gap. Additionally, this paper also looks at potential drawbacks, such as ethics concerns of using student data to power AI. By analyzing the past use of AI in education, this paper seeks to provide a grouping framework to improve understanding of the field and facilitate future development. Framework for organization and further development. This paper intends to look at the past approaches of AI to improve education and categorize them to help guide new development of AI applications in education. The potential benefits of AI-powered education is noteworthy as the current economy is based on higher education. AI can be used to speed up labor-intensive tasks and help close the knowledge gap. Additionally, this paper also looks at potential drawbacks, such as ethics concerns of using student data to power AI. By analyzing the past use of AI in education, this paper seeks to provide a grouping framework to improve understanding of the field and facilitate future development.

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Section: Game-based Learningmentioning

confidence: 99%

Exploring the role of AI in education

Nguyen

2023

LJSS

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“…For the baseline methods, the sequences were represented by one-hot encoding vectors, following (Goslen et al 2022b;Min et al 2017). For T5-based models, the same sequences were used in text string format to retain the same amount of information as the baselines.…”

Section: Datamentioning

confidence: 99%

Language Model-Based Player Goal Recognition in Open World Digital Games

Kim,

Goslen,

Rowe

et al. 2023

AIIDE

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Devising models that reliably recognize player goals is a key challenge in creating player-adaptive games. Player goal recognition is the task of automatically recognizing the intent of a player from a sequence of observed player actions in a game environment. In open-world digital games, players often undertake suboptimal and varied sequences of actions to achieve goals, and the high degree of freedom afforded to players makes it challenging to identify sequential patterns that lead toward specific goals. To address these issues, we present a player goal recognition framework that utilizes a fine-tuned T5 language model, which incorporates our novel attention mechanism called Temporal Contrary Attention (TCA). The T5 language model enables the framework to exploit correlations between observations through non-sequential self-attention within input sequences, while TCA enables the framework to learn to eliminate goal hypotheses by considering counterevidence within a temporal window. We evaluate our approach using game trace data collected from 144 players' interactions with an open-world educational game. Specifically, we investigate the predictive capacity of our approach to recognize player goals as well as player plans represented as abstract actions. Results show that our approach outperforms non-linguistic machine learning approaches as well as T5 without TCA. We discuss the implications of these findings for the design and development of player goal recognition models to create player-adaptive games.

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“…Interaction trace log data can be analyzed using machine learning techniques to detect different facets of user engagement, including cognitive [44,[55][56][57], affective [49,58,59], behavioral [60][61][62][63][64], and social [65][66][67] components. Machine learning techniques have been applied to measure a broad range of engagement phenomena, including goal setting [68], learning [44], problem-solving [69], affect [49], and reflection [65] among others. In addition, there has been significant attention toward using machine learning to measure disengagement with technology [63].…”

Section: Measurementmentioning

confidence: 99%

Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions

Giovanelli¹,

Rowe²,

Taylor³

et al. 2023

J Med Internet Res

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Understanding and optimizing adolescent-specific engagement with behavior change interventions will open doors for providers to promote healthy changes in an age group that is simultaneously difficult to engage and especially important to affect. For digital interventions, there is untapped potential in combining the vastness of process-level data with the analytical power of artificial intelligence (AI) to understand not only how adolescents engage but also how to improve upon interventions with the goal of increasing engagement and, ultimately, efficacy. Rooted in the example of the INSPIRE narrative-centered digital health behavior change intervention (DHBCI) for adolescent risky behaviors around alcohol use, we propose a framework for harnessing AI to accomplish 4 goals that are pertinent to health care providers and software developers alike: measurement of adolescent engagement, modeling of adolescent engagement, optimization of current interventions, and generation of novel interventions. Operationalization of this framework with youths must be situated in the ethical use of this technology, and we have outlined the potential pitfalls of AI with particular attention to privacy concerns for adolescents. Given how recently AI advances have opened up these possibilities in this field, the opportunities for further investigation are plenty.

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Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning

Cited by 6 publications

References 14 publications

Exploring the role of AI in education

Exploring the role of AI in education

Language Model-Based Player Goal Recognition in Open World Digital Games

Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions

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