Undergraduate AI Research Articles

Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment

A growing number of courses seek to increase the basic artificial-intelligence skills (“AI literacy”) of their participants. At this time, there is no valid and reliable measurement tool that can be used to assess AI-learning gains. However, the existence of such a tool would be important to enable quality assurance and comparability. In this study, a validated AI-literacy-assessment instrument, the “scale for the assessment of non-experts’ AI literacy” (SNAIL) was adapted and used to evaluate an undergraduate AI course. We investigated whether the scale can be used to reliably evaluate AI courses and whether mediator variables, such as attitudes toward AI or participation in other AI courses, had an influence on learning gains. In addition to the traditional mean comparisons (i.e., t-tests), the comparative self-assessment (CSA) gain was calculated, which allowed for a more meaningful assessment of the increase in AI literacy. We found preliminary evidence that the adapted SNAIL questionnaire enables a valid evaluation of AI-learning gains. In particular, distinctions among different subconstructs and the differentiation constructs, such as attitudes toward AI, seem to be possible with the help of the SNAIL questionnaire.

Incorporating Computational Sustainability into AI Education through a Freely-Available, Collectively-Composed Supplementary Lab Text


 
 
 We introduce a laboratory text on environmental and societal sustainability applications that can be a supplemental resource for any undergraduate AI course. The lab text, entitled Artificial Intelligence for Computational Sustainability: A Lab Companion, is brand new and incomplete; freely available through Wikibooks; and open to community additions of projects, assignments, and explanatory material on AI for sustainability. The project adds to existing educational efforts of the computational sustainability community, encouraging the flow of knowledge from research to education and public outreach. Besides summarizing the laboratory book, this paper touches on its implications for integration of research and education, for communicating science to the public, and other broader impacts.
 
 

A Hands-on Project for Teaching Semantic Web Technologies in an Undergraduate AI Course

A Hands-on Project for Teaching Semantic Web Technologies in an Undergraduate AI Course

AI education matters

In this column, we recount the history of EAAI mentored undergraduate research challenges from 2014 through the present and share a vision of how such offerings may become more diverse and engage a broader range of faculty mentors and undergraduate researchers. Unlike many academic disciplines, Computer Science undergraduate majors currently are not usually required to take or even offered a research methods course. Even so, many graduate schools desire to admit graduate students with undergraduate research experience. The EAAI Symposium has historically affirmed the value of mentored undergraduate research as an important part of undergraduate AI education. It has expressed this value through the support of a number of mentored undergraduate research challenges, described below.

Minecraft as a Platform for Project-Based Learning in AI

Undergraduate courses that focus on open-ended, project-based learning teach students how to define concrete goals, transfer conceptual understanding of algorithms to code, and evaluate/analyze/present their solution. However, AI, along with machine learning, is getting increasingly varied in terms of both the approaches and applications, making it challenging to design project courses that span a sufficiently wide spectrum of AI. For these reasons, existing AI project courses are restricted to a narrow set of approaches (e.g. only reinforcement learning) or applications (e.g. only computer vision).In this paper, we propose to use Minecraft as the platform for teaching AI via project-based learning. Minecraft is an open-world sandbox game with elements of exploration, resource gathering, crafting, construction, and combat, and is supported by the Malmo library that provides a programmatic interface to the player observations and actions at various levels of granularity. In Minecraft, students can design projects to use approaches like search-based AI, reinforcement learning, supervised learning, and constraint satisfaction, on data types like text, audio, images, and tabular data. We describe our experience with an open-ended, undergraduate AI projects course using Minecraft that includes 82 different projects, covering themes that ranged from navigation, instruction following, object detection, combat, and music/image generation.

AI education matters

Prolog is a great language to include in an undergraduate AI course. Its logical programming paradigm challenges students to write code in a way that is usually very different from their other courses. Students struggle with its approach but, in my experience, come to appreciate its ability to handle a broad range of AI tasks elegantly.

An Image Wherever You Look! Making Vision Just Another Sensor for AI/Robotics Projects

Visual sensing can be difficult to incorporate into undergraduate robotics and AI assignments. Images, after all, do not provide a direct estimate of the geometric conditions within the field of view. Yet vision is increasingly compelling as a part of undergraduate AI and robotics, given the centrality of pixels in our students' interactions with technology and each other. This paper shares a small-footprint framework designed to make visual sensing as easy to incorporate into AI projects and assignments, e.g., as a source of evidence for localization algorithms, as range sensors. The framework leverages (hand-built) circular panoramas and the image-matching capabilities provided by OpenCV's python library. An example localization project highlights its pedagogical accessibility and ease of deployment atop low-cost hardware and alongside other sensors.

Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education

This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the trade-offs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI.

Non-traditional projects in the undergraduate AI course

Non-traditional projects in the undergraduate AI course

An expert system development environment for introductory AI course projects

This paper describes the use of an expert system development environment as a teaching environment to support a cognitive simulation project in an undergraduate introductory AI course. The use of this development environment, which was originally intended for experienced production system programmers, as a teaching tool for novices, presented challenges on a number of levels. This paper describes the development environment and demonstrates how it can be used to help a student understand programming lesson points, as well as how a student can use it to better understand how a particular system is behaving and whether it matches the students understanding of what it is supposed to be doing.

The introductory undergraduate AI course as observed on WWW

Most people who have taught the introductory undergraduate course in Artificial Intelligence agree that it is a difficult course to teach well. Recently attention has been directed to achieving a better understanding of the objectives, prerequisites, themes, pedagogic methodologies, topics, and support resources for this course [1]. Also, recently a number of professors who teach the course, acting independently have created and made available on the World Wide Web (WWW) simple Course Support Environments (CSEs) which include both pedagogic and administrative information.

The Odyssey of an Undergraduate AI Experiment

Abstract Now that more powerful 32‐bit microcomputers with fast PROLOG interpreters are available, a reasonable introduction to artificial intelligence at the liberal arts undergraduate level can and should be made. This paper details how an experiment in offering a directed‐reading course in expert systems at a small liberal arts undergraduate university gave rise to the following: The development of a prototype diagnostic expert system on alcoholism and drug abuse, as a follow‐up independent study project An award‐winning essay on ‘The Future of Artificial Intelligence’ Departmental curriculum changes The submission by an undergraduate of a paper entitled ‘SOCRATES: A Project Integrating Human Science with Computer Science’