AI Education: Open-Access Educational Resources on AI

Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning experiences for each topic from scratch. In this column, we focus on a few online resources that we would recommend to AI Educators looking to find good starting points for course development. [excerpt

The State of American Indian & Alaska Native Education in California, Executive Summary 2014

The findings from the CICSC'S 2012 State of AI/AN Education in California Report confirmed the need for greater efforts to prepare, to recruit, to retain, and to graduate Native youth from institutions of higher education. In particular, the realization that AI/AN enrollment rates are declining across the CSUs was alarming. These results provided the basis to delve deeper into the program, outreach, and support at postsecondary institutions in the 2014 report to determine where enrollment and transfer numbers are decreasing or increasing; to determine what the best practices at state colleges and universities to attract, retain, and graduate AI/ANs are; and correspondingly to determine where we, as educators of AI/AN students in the state of California, need to improve

AI Education Matters: Teaching Hidden Markov Models

In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt

Opportunities and challenges in using AI Chatbots in Higher Education

Artificial intelligence (AI) conversational chatbots have gained popularity over time, and have been widely used in the fields of e-commerce, online banking, and digital healthcare and well-being, among others. The technology has the potential to provide personalised service to a range of consumers. However, the use of chatbots within educational settings is still limited. In this paper, we present three chatbot prototypes, the Warwick Manufacturing Group, University of Warwick, are currently developing, and discuss the potential opportunities and technical challenges we face when considering AI chatbots to support our daily activities within the department. Three AI virtual agents are under development: 1) to support the delivery of a taught Master's course simulation game; 2) to support the training and use of a newly introduced educational application; 3) to improve the processing of helpdesk requests within a university department. We hope this paper is informative to those interested in using chatbots in the educational domain. We also aim to improve awareness among those within the chatbot development industry, in particular the chatbot engine providers, about the educational and operational needs within educational institutes, which may differ from those in other domains

Expert systems and developing expertise: Implications of Artificial Intelligence for Education

This paper discusses a few issues in AI research with the aim of understanding whether the concepts or the tools of AI can be of use in education (see also Green, 1984). Most of the discussion focuses on natural language understanding, one aspect of the highly diverse field of AI.published or submitted for publicationis peer reviewe

The State of American Indian & Alaska Native Education in California 2014

The findings from the California Indian Culture and Sovereignty Center's 2012 report confirmed the need for greater efforts to prepare, to recruit, to retain, and to graduate Native youth from institutions of higher education. In particular, the realization that AI/AN enrollment rates are declining across the CSUs was alarming. These results provided the basis to delve deeper into the program, outreach, and support of postsecondary institutions in the 2014 report to determine where enrollment and transfer numbers are decreasing or increasing; to determine what the best practices at state colleges and universities to attract, retain, and graduate AI/ANs are; and correspondingly to determine where we, as educators of AI/AN students in the state of California, need to improve

AI Education: Machine Learning Resources

In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In ournext column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above. [excerpt

AI Education: Birds of a Feather

Games are beautifully crafted microworlds that invite players to explore complex terrains that spring into existence from even simple rules. As AI educators, games can offer fun ways of teaching important concepts and techniques. Just as Martin Gardner employed games and puzzles to engage both amateurs and professionals in the pursuit of Mathematics, a well-chosen game or puzzle can provide a catalyst for AI learning and research. [excerpt

Model AI Assignments 2018

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu