SBIR Phase I: Automated Detection of Confounds and Inappropriate Context to Promote Prosocial Learning and Cognition

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是开发基于人工智能（AI）的算法，以可承受的成本为文字视频游戏生成内容。单词游戏通过自适应词汇缩放系统和为有视觉或运动障碍的玩家提供的可访问性选项，支持所有年龄和背景的玩家的读写能力、流畅性、批判性思维和跨文化理解。科学、技术、工程和数学（STEM）素养是通过将STEM内容与娱乐和严肃内容相结合来支持的。这些亲社会和认知影响对于个人和职业成长、文化能力至关重要，并将由游戏学习研究人员进行测量。该项目还将通过向开源资源添加新的基准，为自然语言处理和机器学习领域做出贡献。成功降低内容制作成本可能会导致将内容授权给其他游戏发行商，并在市场上创造更多的文字游戏，从而使玩家受益。这个项目的独特之处在于，它可以帮助美国游戏行业留住工作岗位，并为该行业的发展做出贡献。该项目的技术创新有三个方面：1)开发不相关单词内容生成的算法，2)开发自然语言内容的适当性和冒犯性过滤器，以及3)评估单词游戏提高认知功能和社会意识的能力。这项研究和发展有可能解决自然语言处理领域在不相关性方面的空白。这项工作的一部分有助于为未来的研究提供开源基准。同样，社会偏见在机器学习模型中是一个普遍而众所周知的问题，需要通过新开发的算法来检测和避免潜在的冒犯性或不恰当的单词组合，以明确检测和避免发布此类内容。为了实现这两个目标，将采用各种机器学习技术，包括利用现有大型自然语言模型的技术，并对其准确性进行评估。使用这些算法作为内容创建的基础，字谜游戏将整合生成的内容。通过扩大词汇量，提高社会意识和自信心的能力将得到评价。具体来说，该研究将评估短期玩法和长期玩法，并通过游戏内部参数和调查衡量其有效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the development of Artificial Intelligence (AI)-based algorithms that generate content for word-meaning video games at an affordable cost. Word-meaning games support literacy, fluency, critical thinking, and cross-cultural understanding for players of all ages and backgrounds via adaptive vocabulary scaling systems and accessibility options for players with visual or motor difficulties. Science, Technology, Engineering, and Mathematics (STEM) literacy is supported by incorporating STEM content in a mix of entertaining and serious content. These prosocial and cognitive impacts are essential for personal and professional growth, cultural competence, and will be measured by game learning researchers. The project will also contribute to the field of natural language processing and machine learning through the addition of new benchmarks to open-source resources. Success in reducing content creation costs could lead to licensing content to other game publishers and the creation of additional word-meaning games on the market, benefiting players. This project is uniquely positioned to help retain game industry jobs in the U.S. and contribute to the growth of the industry.The technical innovation of the project is threefold: 1) development of algorithms for unrelated word content generation, 2) development of appropriateness and offensiveness filters for natural language content, and 3) evaluation of a word-meaning game’s ability to improve cognitive function and social awareness. This research and development has the potential to address a gap in the field of natural language processing on unrelatedness. Part of this effort contributes to open-source benchmarks for future research. Similarly, social bias is a prevalent and well-known issue in machine learning models, potentially offensive or inappropriate word combinations need to be detected and avoided via newly developed algorithms that explicitly detect and avoid publishing such content. To achieve both goals, a variety of machine learning techniques, including those that leverage existing large natural language models, will be employed and evaluated for accuracy. Using these algorithms as a foundation for content creation, the word-meaning game will integrate the generated content. The program will be evaluated with regard to its ability to increase social awareness and confidence with an expanding vocabulary. Specifically, the study will evaluate both brief gameplay and long-term gameplay and measure efficacy with in-game metrics and surveys.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.