Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.

Augmenting immersive technologies with AI for foreign language learning is a relatively unexplored, multidisciplinary, and complex research paradigm. The Cognitive and Immersive Room at Rensselaer Polytechnic Institute facilitates cultural and foreign language learning beyond a traditional classroom setting. Exploration of virtual environments and real-world renderings occur at human-scale by groups of students and teachers simultaneously, without the need for head-mounted displays. Students travel to true-to-life Panoramic Scenes to learn authentic cultural knowledge and practice vocabulary within the same surroundings they would use such knowledge while traveling abroad. Multimodal input allows students to engage with the system through gesture, voice, and spatial positioning, creating a dynamic language learning experience. This work underwent initial assessment in a six-week summer course, AI-Assisted Immersive Chinese, and preliminary qualitative results find a majority of surveyed students indicate the system is useful, engaging, and fun.

We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies - qualitative interviews, a controlled experiment, and a card-sorting task - to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool.

The Rensselaer Mandarin Project enables a group of foreign language students to improve functional understanding, pronunciation and vocabulary in Mandarin Chinese through authentic speaking situations in a virtual visit to China. Students use speech, gestures, and combinations thereof to navigate an immersive, mixed reality, stylized realism game experience through interaction with AI agents, immersive technologies, and game mechanics. The environment was developed in a black box theater equipped with a human-scale 360◦ panoramic screen (140h, 200r), arrays of markerless motion tracking sensors, and speakers for spatial audio.

As cities around the world become more diverse in culture and language, there is a growing need for learning foreign languages. To further this excitement, we have built a human-scale, immersive room with a virtual AI agent that aids foreign language learning. Our system aids the language learning process through task-completion exercises using multi-modal dialogue. The Cognitive and Immersive Room (CIR) is developed as an immersive Chinese restaurant to teach Mandarin, but the interaction challenges and solutions can be reasonably generalized to other languages taught using similar techniques. As users interact with the immersive environment and the virtual AI agent, they face several user interaction challenges. These challenges arise from new learners' lack of proficiency in the foreign language. By studying user interactions in the CIR, we were able to articulate some of the interaction challenges. We have enhanced the AI agent, virtual environment, and the on-boarding process for new users to mitigate these challenges. The enhancements and the results which show that they were effective are discussed here.