Game-Based Learning for Autism: A Tabletop Approach to Theory of Mind

This paper presents a conceptual prototype of "Guess the Animal," a tabletop game designed to strengthen Theory of Mind (ToM), particularly perspective-taking and empathy, in autistic children aged 7 to 12. The authors ground their design in three frameworks: game-based learning, Understanding by Design (backward planning from learning goals), and Universal Design for Learning, which shapes multimodal and low-pressure participation. The paper motivates the work by noting the well-documented gap in false-belief reasoning between autistic and non-autistic children, citing a classic early finding that only 20% of autistic children passed a false-belief task compared with 85% of peers, and by arguing that tangible, turn-based play may offer advantages over existing screen-based serious games.

The game centers on asymmetric clue interpretation: each player adopts an animal character and receives comparative clues about a hidden "culprit" (for example, "This animal is bigger than me"), requiring players to reason from another perspective. Prosocial behaviors such as helping or yielding turns are rewarded with tokens, and shared deduction across three rounds is intended to prompt second-order reasoning. A mapping table links each mechanic to a targeted ToM subskill. Gameplay accommodates two to nine players with an adult moderator, using a set of ten animal cards defined by size, color, and tail length, along with clue cards, individual boards, and reward tokens.

Assessment is proposed but not yet conducted. The authors outline pre- and post-testing with standardized false-belief tasks, caregiver reports using the Theory of Mind Inventory, behavioral observation, and feedback from children and caregivers. The paper is explicit that no empirical testing has been carried out, so claims of effectiveness remain preliminary, and it plans a small-scale pilot study with IRB approval and informed consent. The authors position the prototype as a classroom-friendly starting point, with future work needed on adaptability, long-term impact, and scalability.