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With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.

 

Identifying misconceptions in student programming solutions is an important step in evaluating their comprehension of fundamental programming concepts. While misconceptions are latent constructs that are hard to evaluate directly from student programs, logical errors can signal their existence in students’ understanding. Tracing multiple occurrences of related logical bugs over different problems can provide strong evidence of students’ misconceptions. This study presents preliminary results of utilizing an interpretable state-ofthe- art Abstract Syntax Tree-based embedding neural network to identify logical mistakes in student code. In this study, we show a proof-of-concept of the errors identified in student programs by classifying correct versus incorrect programs. Our preliminary results show that our framework is able to automatically identify misconceptions without designing and applying a detailed rubric. This approach shows promise for improving the quality of instruction in introductory programming courses by providing educators with a powerful tool that offers personalized feedback while enabling accurate modeling of student misconceptions. 

Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.

 

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.

 

The growing ubiquity of artificial intelligence (AI) is reshaping much of daily life. This in turn is raising awareness of the need to introduce AI education throughout the K-12 curriculum so that students can better understand and utilize AI. A particularly promising approach for engaging young learners in AI education is game-based learning. In this work, we present our efforts to embed a unit on AI planning within an immersive game-based learning environment for upper elementary students (ages 8 to 11) that utilizes a scaffolding progression based on the Use-Modify-Create framework. Further, we present how the scaffolding progression is being refined based on findings from piloting the game with students.