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1. Evaluating Human Expert Knowledge in Damage Assessment Using Eye Tracking: A Disaster Case Study

   https://doi.org/10.3390/buildings14072114  

   Saleem, Muhammad Rakeh; Mayne, Robert; Napolitano, Rebecca (July 2024, Buildings)

   The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts. 

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2. Beyond Black-Boxing: Building Intuitions of Complex Machine Learning Ideas Through Interactives and Levels of Abstraction

   https://doi.org/10.1145/3501709.3544273  

   Broll, Brian; Grover, Shuchi; Babb, Derek (August 2022, The 2022 ACM Conference on International Computing Education Research V.2 (ICER ’22))

   Existing approaches to teaching artificial intelligence and machine learning often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe advanced ML topics, such as optimization and adversarial examples, can be learned by early high school age students given appropriate support. Our approach focuses on enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. 

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3. From Data Work to Data Science: Getting Past the Gatekeepers

   https://doi.org/10.1145/3568812.3603468  

   Schenck, Lara L.; DiSalvo, Betsy (August 2023, ACM)

   While much computing education research focuses on formal K-12 and undergraduate CS education, a growing body of work is exploring alternative pathways to computing careers [7, 16], alternative outcomes for computing education [15], and adult learning in workplace communities [9, 13]. Within this context, we are studying novice-friendly computational work as a pathway to computing careers. Novice-friendly computational work is a phrase we use to describe computing activities that have a low barrier to entry, are used in authentic contexts outside formal CS spaces, and are legitimate computational activities, e.g., data work [13], web design [5], and Salesforce CRM [9]. Learning through authentic work practices is a promising pathway to computing careers because it poses lower financial and findability barriers than coding bootcamps [14] and online courses [4]. However, gatekeeping culture in computing deems novice-friendly tools like Excel, HTML/CSS, and JSON distinct from “real” programming [12]. Further, novice workers may not be considered legitimate peripheral members of computing communities of practice despite engaging in legitimate computational work [6, 11]. 

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4. SaIL: saliency-driven injection of ARIA landmarks

   https://doi.org/10.1145/3377325.3377540  

   Aydin, Ali Selman; Feiz, Shirin; Ashok, Vikas; Ramakrishnan, IV (January 2020, 25th International Conference on Intelligent User Interfaces)

   Navigating webpages with screen readers is a challenge even with recent improvements in screen reader technologies and the increased adoption of web standards for accessibility, namely ARIA. ARIA landmarks, an important aspect of ARIA, lets screen reader users access different sections of the webpage quickly, by enabling them to skip over blocks of irrelevant or redundant content. However, these landmarks are sporadically and inconsistently used by web developers, and in many cases, even absent in numerous web pages. Therefore,we propose SaIL, a scalable approach that automatically detects the important sections of a web page, and then injects ARIA landmarks into the corresponding HTML markup to facilitate quick access to these sections. The central concept underlying SaIL is visual saliency, which is determined using a state-of-the-art deep learning model that was trained on gaze-tracking data collected from sighted users in the context of web browsing. We present the findings of a pilot study that demonstrated the potential of SaIL in reducing both the time and effort spent in navigating webpages with screen readers. 

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5. Beyond black-boxes: teaching complex machine learning ideas through scaffolded interactive activities

   Broll, Brian; Grover, Shuchi (January 2023, The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23))

   Existing approaches to teaching artifcial intelligence and machine learning (ML) often focus on the use of pre-trained models or fne-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our AI & Cybersecurity for Teens suite of curricular activities aimed at high school students and teachers. 

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