A longstanding goal in computational educational research is to develop explainable knowledge tracing (KT) models. Deep Knowledge Tracing (DKT), which leverages a Recurrent Neural Network (RNN) to predict student knowledge and performance on exercises, has been proposed as a major advancement over traditional KT methods. Several studies suggest that its performance gains stem from its ability to model bidirectional relationships between different knowledge components (KCs) within a course, enabling the inference of a student's understanding of one KC from their performance on others. In this paper, we challenge this prevailing explanation and demonstrate that DKT's strength lies in its implicit ability to model prerequisite relationships as a causal structure, rather than bidirectional relationships. By pruning exercise relation graphs into Directed Acyclic Graphs (DAGs) and training DKT on causal subsets of the Assistments dataset, we show that DKT's predictive capabilities align strongly with these causal structures. Furthermore, we propose an alternative method for extracting exercise relation DAGs using DKT's learned representations and provide empirical evidence supporting our claim. Our findings suggest that DKT's effectiveness is largely driven by its capacity to approximate causal dependencies between KCs rather than simple relational mappings.

Food logging, both self-directed and prescribed, plays a critical role in uncovering correlations between diet, medical, fitness, and health outcomes. Through conversations with nutritional experts and individuals who practice dietary tracking, we find current logging methods, such as handwritten and app-based journaling, are inflexible and result in low adherence and potentially inaccurate nutritional summaries. These findings, corroborated by prior literature, emphasize the urgent need for improved food logging methods. In response, we propose SnappyMeal, an AI-powered dietary tracking system that leverages multimodal inputs to enable users to more flexibly log their food intake. SnappyMeal introduces goal-dependent follow-up questions to intelligently seek missing context from the user and information retrieval from user grocery receipts and nutritional databases to improve accuracy. We evaluate SnappyMeal through publicly available nutrition benchmarks and a multi-user, 3-week, in-the-wild deployment capturing over 500 logged food instances. Users strongly praised the multiple available input methods and reported a strong perceived accuracy. These insights suggest that multimodal AI systems can be leveraged to significantly improve dietary tracking flexibility and context-awareness, laying the groundwork for a new class of intelligent self-tracking applications.

As low-resourced languages are increasingly incorporated into NLP research, there is an emphasis on collecting large-scale datasets. But in prioritizing quantity over quality, we risk 1) building language technologies that perform poorly for these languages and 2) producing harmful content that perpetuates societal biases. In this paper, we investigate the quality of Machine Translation (MT) datasets for three low-resourced languages--Afan Oromo, Amharic, and Tigrinya, with a focus on the gender representation in the datasets. Our findings demonstrate that while training data has a large representation of political and religious domain text, benchmark datasets are focused on news, health, and sports. We also found a large skew towards the male gender--in names of persons, the grammatical gender of verbs, and in stereotypical depictions in the datasets. Further, we found harmful and toxic depictions against women, which were more prominent for the language with the largest amount of data, underscoring that quantity does not guarantee quality. We hope that our work inspires further inquiry into the datasets collected for low-resourced languages and prompts early mitigation of harmful content. WARNING: This paper contains discussion of NSFW content that some may find disturbing.

This paper examines how decision makers in academia, government, business, and civil society navigate questions of power in implementations of artificial intelligence (AI). The study explores how individuals experience and exercise "levers of power," which are presented as social mechanisms that shape institutional responses to technological change. The study reports on the responses of personalized questionnaires designed to gather insight on a decision maker's institutional purview, based on an institutional governance framework developed from the work of Neo Institutionalists. Findings present the anonymized, real responses and circumstances of respondents in the form of twelve fictional personas of high-level decision makers from North America and Europe. These personas illustrate how personal agency, organizational logics, and institutional infrastructures may intersect in the governance of AI. The decision makers' responses to the questionnaires then inform a discussion of the field level personal power of decision-makers, methods of fostering institutional stability in times of change, and methods of influencing institutional change in the field of AI. The final section of the discussion presents a table of the dynamics of the levers of power in the field of AI for change makers and 5 testable hypotheses for institutional and social movement researchers. In summary, this study provides insight on the means for policymakers within institutions and their counterparts in civil society to personally engage with AI governance.

This thesis introduces the Haptic-Audio Code Interface (HACI), an educational tool designed to enhance programming education for visually impaired (VI) students by integrating haptic and audio feedback to compensate for the absence of visual cues. HACI consists of a non-resource-intensive web application supporting JavaScript program development, execution, and debugging, connected via a cable to an Arduino-powered glove with six integrated haptic motors to provide physical feedback to VI programmers. Motivated by the need to provide equitable educational opportunities in computer science, HACI aims to improve non-visual code navigation, comprehension, summarizing, editing, and debugging for students with visual impairments while minimizing cognitive load. This work details HACI's design principles, technical implementation, and a preliminary evaluation through a pilot study conducted with undergraduate Computer Science students. Findings indicate that HACI aids in the non-visual navigation and understanding of programming constructs, although challenges remain in refining feedback mechanisms to ensure consistency and reliability, as well as supplementing the current functionality with a more feature-reach and customizable accessible learning experience which will allow visually impaired students to fully utilize interleaved haptic and audio feedback. The study underscores the transformative potential of haptic and audio feedback in educational practices for the visually impaired, setting a foundation for future research and development in accessible programming education. This thesis contributes to the field of accessible technology by demonstrating how tactile and auditory feedback can be effectively integrated into educational tools, thereby broadening accessibility in STEM education.

Causal structure learning has long been the central task of inferring causal insights from data. Despite the abundance of real-world processes exhibiting higher-order mechanisms, however, an explicit treatment of interactions in causal discovery has received little attention. In this work, we focus on extending the causal additive model (CAM) to additive models with higher-order interactions. This second level of modularity we introduce to the structure learning problem is most easily represented by a directed acyclic hypergraph which extends the DAG. We introduce the necessary definitions and theoretical tools to handle the novel structure we introduce and then provide identifiability results for the hyper DAG, extending the typical Markov equivalence classes. We next provide insights into why learning the more complex hypergraph structure may actually lead to better empirical results. In particular, more restrictive assumptions like CAM correspond to easier-to-learn hyper DAGs and better finite sample complexity. We finally develop an extension of the greedy CAM algorithm which can handle the more complex hyper DAG search space and demonstrate its empirical usefulness in synthetic experiments.

Kim Kardashian blames ChatGPT for law school test failures while Miami-Dade Sheriff's Office tests America's first autonomous police vehicle in the latest AI news.

The site, a few kilometers from her own hut in Ipatsé, a Kuikuro village in the Xingu Indigenous territory, was once the backyard of her great-grandparents' house. As she scrapes the brown earth with a trowel, she soon spots a black ceramic shard. It is only about the size of her palm, and this is her first day ever on an archaeological excavation. But she immediately recognizes what the object once was. "It's an alato," she says, showing the piece to a group of archaeologists and other Kuikuro who have gathered to watch the excavation in the village of Anitahagu. An alato, Yamána explains, is a large pan used to cook beiju, a white flatbread made with yucca flour that's eaten almost every day in her village. Her grandmother still has one in the backyard fire pit where she prepares most meals, just as countless Kuikuro women did before her. This alato likely belonged to her great-grandmother on her mother's side.

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We drank adaptogen drinks for weeks, and taste-tested with a trained sommelier. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. The best adaptogen drinks promise not just to wake you up in the morning, but offer focus and clarity and maybe even a warm wash of well-being. A different drink might tuck you gently in at night, or sub in for alcohol as a mindful party drink. I've spent months trying some of the most popular functional drinks on the market, bedding down with kava or tryptophan-laced xicha morada, and waking up with caffeine and L-theanine. Many of the new school of nootropic and functional drinks are like kissing cousins of mushroom coffee, except in refreshing soda form. Functional sodas might be chockablock with mushroom adaptogens such as reishi and cordyceps, alongside traditional home anxiety remedies such as ashwagandha or L-theanine. I both logged the effects of each soda, and held a large taste test with Portland, Oregon, sommelier Sami Gaston, owner of an excellent wine bar and shop called Bar Diane and Negociant, respectively--to determine how happy you'd be to drink them even if they didn't help you focus better on endless spreadsheets or the hunt for a job. Also check out WIRED's guide to mushroom gummies, or take your wellness in powdered form with the best greens powders and the best protein powders .