Artificial intelligence system architects can increase user trust by designing systems that are inherently transparent. We propose the idea of representing an AI system as an amalgamation of the AI Model (algorithms), data (input and output, including outcomes), and the user interface with visual interpretations (e.g. graphs, Venn diagrams). By designing human controls and feedback mechanisms for AI systems that allow users to exert control over them we can integrate transparency into existing user interfaces. Our plan is to design prototypes of transparent user interfaces for AI systems using well-known usability principles. By conducting surveys we will study their impact to see if these principles help the user to work with the AI system with confidence and if the user perceives the system to be adequately transparent.

Recent advances in artificial intelligence (AI) systems have resulted in their ability to provide precise recommendations in response to users’ questions (prompts). However, AI models are opaque, making it challenging for users to comprehend their inner workings. While the Human-Computer Interaction (HCI) community has advocated for design principles like progressive disclosure to improve transparency, we still lack empirical evidence validating its efficacy, especially in the context of LLM-based text generation. Addressing this gap, our paper delves into a user study aimed at investigating the effect of progressive disclosure and adjusting the explanations so as to adapt to users’ mental models for improving the transparency of AI text generation systems.

One of the biggest reasons artificial intelligence (AI) gets a backlash is because of inherent biases in AI software. Deep learning algorithms use data fed into the systems to find patterns to draw conclusions used to make application decisions. Patterns in data fed into machine learning algorithms have revealed that the AI software decisions have biases embedded within them. Algorithmic audits can certify that the software is making responsible decisions. These audits verify the standards centered around the various AI principles such as explainability, accountability, human-centered values, such as, fairness and transparency, to increase the trust in the algorithm and the software systems that implement AI algorithms.

We developed a hands-on lab for students to learn macro malware detection using decision trees on an open sourced data analytics software, HPCC, running on a virtual machine. The labware along with required software and documents are freely available at our project website. Our design is low cost, customizable and can be easily replicated to other institutions. Our experience shows that students have difficulties in configuring software environment for the lab. The virtual machine approach allows pre-configurations for datasets and required software installations and setting to facilitate students' learning and instructors' teaching. Since students are working on malware, the virtual machine provides an isolated network environment for testing without affecting normal operating network. This lab design can also be integrated into online courses by simply downloading and installing the customized virtual machine.

This paper summarizes our mid-project results in developing and evaluating a CS Principles (CSP) professional development (PD) model for training several cohorts of teachers across an entire state geography. CS4Alabama is an NSF-funded project that has adopted the successful practices of a national AP training program developed by the National Math and Science Initiative (NMSI) in pursuit of scalable deployment and sustainable persistence of new CSP courses across Alabama. We have created a CSP curriculum and PD program based on year-long in-person training and distance learning collaboration, which also was offered as a national MOOC as part of Google CS4HS. A statewide Teacher Leader (TL) model is used, where those who have previously taught rigorous CS courses serve as mentors in training new peer cohorts as they establish CSP courses in their schools. Teachers in these cohorts collaborate together on content and pedagogical learning experiences, fostered by the TLs. This paper reports on assessment results that are uncovering the facets of our model that are most suitable for building a sustainable network of CSP teachers. The paper summarizes our PD model, offers various lessons learned, and details the findings of the project's external evaluation team.

POGIL (Process Oriented Guided Inquiry Learning) is based on the principle that students learn more when they construct their own understanding. Instead of attending lecture, student teams work through POGIL activities to discover concepts on their own, while instructors circulate and facilitate learning. Students learn the material better, and this constructivist approach also teaches them important process skills, including critical thinking, team work, and leadership. In this special session, SIGCSE attendees will experience a POGIL activity for themselves, learn about the structure of POGIL activities (through a POGIL meta-activity), and hear perspectives from teachers who recently adopted POGIL activities available at http://cspogil.org. We will share classroom-tested guided inquiry activities and discuss how POGIL can transform CS classes at all levels, from high school to graduate-level classes, from small schools to large universities.

This poster describes our first-year experience in developing and evaluating a CS Principles professional development model for training a cohort of teachers across an entire state geography. The scalable deployment and sustainable persistence of the new CS Principles course adopts the successful practices of a national AP training program developed by the National Math and Science Initiative (NMSI). It applies those practices in a professional development program based on year-long in-person training and distance learning collaboration. A statewide "Teacher Leader" model is being explored where those already teaching more rigorous CS courses will assist in training new peer cohorts as they establish CS Principles in their schools. Teachers in these cohorts collaborate together on content and pedagogical learning experiences, fostered by peer leaders. The assessment is uncovering the facets of our model that are most suitable for building a sustainable network of CS Principles teachers. This poster is focused on a description of our first-year experiences and extensive evaluation results. The poster will describe the curriculum that is being developed by our teachers, a set of lessons learned from our PD experiences, and the details of our evaluation. The Teacher Leaders will be available near the poster to discuss their experience with anyone who has an interest in our professional development model for CS Principles.

In this paper we analyze the CS Principles project, a proposed Advanced Placement course, by focusing on the second pilot that took place in 2011-2012. In a previous publication the first pilot of the course was explained, but not in a context related to relevant educational research and philosophy. In this paper we analyze the content and the pedagogical approaches used in the second pilot of the project. We include information about the third pilot being conducted in 2012-2013 and the portfolio exam that is part of that pilot. Both the second and third pilots provide evidence that the CS Principles course is succeeding in changing how computer science is taught and to whom it is taught.

This poster presents recent progress with the Computing Educators Oral History Project (CEOHP). To provide context, the poster includes a brief history of CEOHP and a summary of the interviews currently in the collection (a total of twenty-six completed interviews at the end of 2011). During Evaluation efforts in 2009 and 2010, a series of face-to-face and on-line qualitative interviews provided feedback about the CEOHP website, including its organization, content, and usability by target groups. The Evolution process incorporated ideas generated during Evaluation to improve both quality and consistency of the collection elements and the CEOHP website. In the Archival Storage process, all completed interviews in the CEOHP collection are being submitted to the prestigious Charles Babbage Institute for permanent indexing and archiving. These archival steps will become part of CEOHP's standard processing procedures, so each interview added to the CEOHP collection will be archived by the Charles Babbage Institute, guaranteeing that these materials will survive far into the future. This poster describes major steps, challenges, and achievements in the areas of Evaluation, Evolution, and Archival Storage, as well as the spiral nature of these activities as part of the on-going process of growing a collection such as CEOHP.

The Computing Educators Oral History Project (CEOHP) is a collection of interviews with computing educators. During 2010, evaluation efforts guided major updates of the CEOHP website (ceohp.org) and the addition of new sections with activities appropriate for use in secondary and post-secondary classes. This poster introduces the CEOHP project, describes the pedagogical aspects of the site, and suggests uses for the educational materials.