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

SBIR 第一阶段：自动检测混淆和不适当的背景，以促进亲社会学习和认知

• 批准号: 2304423
• 负责人: Michael Douma
• 金额: $ 27.5万
• 依托单位: OTHERWORDLY LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304423&HistoricalAwards=false
• 关键词: SBIR; Phase; Automated; Detection; Confounds

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.

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意义的视频游戏生成内容。词义游戏通过自适应词汇缩放系统和视觉或运动障碍玩家的可访问性选项，为所有年龄和背景的玩家提供识字，流畅性，批判性思维和跨文化理解。科学，技术，工程和数学（STEM）素养通过将STEM内容纳入娱乐和严肃内容的混合来支持。这些亲社会和认知的影响是必不可少的个人和专业成长，文化能力，并将由游戏学习研究人员进行测量。该项目还将通过为开源资源增加新的基准，为自然语言处理和机器学习领域做出贡献。成功降低内容创建成本可能会导致将内容授权给其他游戏发行商，并在市场上创建更多的词义游戏，使玩家受益。该项目的技术创新有三个方面：1）开发用于生成无关词语内容的算法; 2）开发用于自然语言内容的适当性和攻击性过滤器; 3）评估词义游戏改善认知功能和社会意识的能力。这项研究和开发有可能解决自然语言处理领域在无关性方面的空白。 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，社交偏见是机器学习模型中普遍存在的众所周知的问题，需要通过新开发的算法来检测和避免潜在的冒犯性或不适当的单词组合，这些算法可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，词义游戏将集成生成的内容。 该计划将评估其通过扩大词汇量来提高社会意识和信心的能力。具体而言，该研究将评估短期游戏和长期游戏，并通过游戏内指标和调查来衡量效果。该奖项反映了NSF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。