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

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

Verbal analogy problem sets: An inventory of testing materials.

Analogical reasoning is an active topic of investigation across education, artificial intelligence (AI), cognitive psychology, and related fields. In all fields of inquiry, explicit analogy problems provide useful tools for investigating the mechanisms underlying analogical reasoning. Such sets have been developed by researchers working in the fields of educational testing, AI, and cognitive psychology. However, these analogy tests have not been systematically made accessible across all the relevant fields. The present paper aims to remedy this situation by presenting a working inventory of verbal analogy problem sets, intended to capture and organize sets from diverse sources

TOWARDS A WITTGENSTEINEAN LADDER FOR THE UNIVERSAL VIRTUAL CLASSROOM (UVC)

The aim of this work is to move from the foreign dominated to the self-dominated by encouraging people to draw their own conclusions with the help of own rational consideration. Here a room as an environment that is encouraging innovation, which can be denoted as “Innovation Lab”, and making processes as can be regarded as “Smart Lab” is an essential base. The question related to this generalized self-organizational learning method investigated in our paper is how a UVC, which is a room that connects people from different physical places to one synchronous and virtual perceivable place, which is built on these preconditions, can be operated both resource and learning-efficient for both the course participants and the educational organization. A practical approach of implementing a virtual classroom concept, including informative tutorial-feedback, is developed conceptually that also accounts for and implements the results of reinforcement machine-learning methods in AI applications. The difference that makes the difference is gained by reimplementing the AI tools in an AI instrument, in a “Smart Lab” environment and that in the teaching environment. By means of this, a cascaded feedback-loop system is informally installed, which gains feedback at different levels of abstraction. By this learning on each stage, in a collaborative and together decentralized and sequential fashion takes place, as the selforganizational implementations lead implicitly, also by means of the in the course implemented tools, to increasingly self-control. As such in the course, a tool is implemented, as generalizations by means of reinforcement learnings are to be emergently foreseen by this method, which goes beyond the tools, that have already been implemented before. This AI-enhanced learning coevolution shall then, predictively, as well increase the potential of the course participants as the educational organization according to the Wittgensteinean parable: A ladder leading into a selfly-organized future

What Works? A Critique of Appreciative Inquiry as a Research Method/ology

Appreciative Inquiry (AI) has gained prominence as an organizational development approach. For over 15 years, it has had varied use in higher education research as a methodology and as a collection of methods. Perhaps the most consistently used, yet most criticized, aspect of AI is the positive stance that its adherents adopt. In this chapter, we survey the prevalence and use of AI, both in the wider literature and in higher education research. We offer our own case study to illustrate the practicalities of employing it and discuss our findings. We suggest that educational researchers are overlooking relevant AI research published within other disciplines; that our own and other case stories can provide guidance for the use of AI in academic contexts; and that AI’s collaborative and positive standpoint has potential as a research methodology influencing policy

Language Learning and Interactive TV

The integration of engaging TV style content with the individualization and ‘intelligent’ content management offered by techniques from AI has the potential to provide learning environments that are both highly motivating and educationally sound. This paper describes why the area of language learning would be a particularly appropriate domain for interactive educational television to focus on. It also indicates some of the criteria to be fulfilled in order to provide optimal language learning conditions and how these might be satisfied using TV/Film content and techniques from AIED