2 research outputs found
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DoomScroll: Modding Among Us to Combat Misinformation
Misinformation is one society’s most pressing issues, spreading division and chaos across the globe. Videogames have become one of the more promising mediums for misinformation interventions, teaching players common falsehood indicators and helping them develop their discernment abilities. This thesis details the design, playtesting, and evaluation of a digital game intervention intended to improve players’ misinformation discernment abilities. Through a design narrative, it first describes the design goals and development of an initial iteration of DoomScroll, a modified version of the popular social deception game Among Us. It then reports on the playtesting of DoomScroll and how player feedback was used to refine and improve the mod’s gameplay. It concludes by detailing a randomized control trial conducted to more rigorously test DoomScroll’s efficacy as a misinformation intervention tool when compared to a control condition. Player feedback regarding its socialization and replayability suggests that DoomScroll did meet its design goals by improving player motivation and increasing the likelihood of repeated use. However, further analyses showed that the mod did not produce any statistically significant improvement in players’ discernment abilities. The qualitative findings were more positive, suggesting an uneven distribution of process-driven learning did occur. This study contributes insights to improve misinformation game design and into the potential of modding as a medium for educational game development
Aneesah: a novel methodology and algorithms for sustained dialogues and query refinement in natural language interfaces to databases
This thesis presents the research undertaken to develop a novel approach towards the development of a text-based Conversational Natural Language Interface to Databases, known as ANEESAH. Natural Language Interfaces to Databases (NLIDBs) are computer applications, which replace the requirement for an end user to commission a skilled programmer to query a database by using natural language. The aim of the proposed research is to investigate the use of a Natural Language Interface to Database (NLIDB) capable of conversing with users to automate the query formulation process for database information retrieval. Historical challenges and limitations have prevented the wider use of NLIDB applications in real-life environments. The challenges relevant to the scope of proposed research include the absence of flexible conversation between NLIDB applications and users, automated database query building from multiple dialogues and flexibility to sustain dialogues for information refinement. The areas of research explored include; NLIDBs, conversational agents (CAs), natural language processing (NLP) techniques, artificial intelligence (AI), knowledge engineering, and relational databases.
Current NLIDBs do not have conversational abilities to sustain dialogues, especially with regards to information required for dynamic query formulation. A novel approach, ANEESAH is introduced to deal with these challenges. ANEESAH was developed to allow users to communicate using natural language to retrieve information from a relational database. ANEESAH can interact with the users conversationally and sustain dialogues to automate the query formulation and information refinement process. The research and development of ANEESAH steered the engineering of several novel NLIDB components such as a CA implemented NLIDB framework, a rule-based CA that combines pattern matching and sentence similarity techniques, algorithms to engage users in conversation and support sustained dialogues for information refinement. Additional components of the proposed framework include a novel SQL query engine for the dynamic formulation of queries to extract database information and perform querying the query operations to support the information refinement.
Furthermore, a generic evaluation methodology combining subjective and objective measures was introduced to evaluate the implemented conversational NLIDB framework. Empirical end user evaluation was also used to validate the components of the implemented framework. The evaluation results demonstrated ANEESAH produced the desired database information for users over a set of test scenarios. The evaluation results also revealed that the proposed framework components can overcome the challenges of sustaining dialogues, information refinement and querying the query operations