Preparing teachers for the application of AI-powered technologies in foreign language education

As any other area of human lives, current state of foreign language education has been greatly influenced by the latest developments in the modern information communication technologies. The paper focuses specifically on the incorporation of artificial intelligence (AI), which includes a wide range of technologies and methods, such as machine learning, adaptive learning, natural language processing, data mining, crowdsourcing, neural networks or an algorithm, into foreign language learning and teaching. First, the paper is concerned with changes brought to foreign language education specifically through the application of AI-powered tools and discusses ICALL (intelligent computer assisted language learning) as a subset of CALL. Second, it summarizes eight types of AI-powered tools for foreign language education and related results of the existing research, however scarce it is. Third, it discusses the frame for effective preparation of foreign language teachers in order to integrate AI-powered tools into their teaching to make it easier, less time-consuming and more effective. The author argues for reconsideration of the existing frames of requirements for CALL teachers.[KEGA 001TTU-4/2019

"Do Animals Have Accents?": Talking with Agents in Multi-Party Conversation

In this paper we unpack the use of conversational agents, or so-called intelligent personal assistants (IPAs), in multiparty conversation amongst a group of friends while they are socialising in a café. IPAs such as Siri or Google Now can be found on a large proportion of personal smartphones and tablets, and are promoted as ‘natural language’ interfaces. The question we pursue here is how they are actually drawn upon in conversational practice? In our work we examine the use of these IPAs in a mundane and common-place setting and employ an ethnomethodological perspective to draw out the character of the IPA-use in conversation. Additionally, we highlight a number of nuanced practicalities of their use in multi-party settings. By providing a depiction of the nature and methodical practice of their use, we are able to contribute our findings to the design of IPAs

“Do animals have accents?”: talking with agents in multi-party conversation

In this paper we unpack the use of conversational agents, or so-called intelligent personal assistants (IPAs), in multi- party conversation amongst a group of friends while they are socialising in a café. IPAs such as Siri or Google Now can be found on a large proportion of personal smartphones and tablets, and are promoted as ‘natural language’ interfaces. The question we pursue here is how they are actually drawn upon in conversational practice? In our work we examine the use of these IPAs in a mundane and common-place setting and employ an ethnomethodological perspective to draw out the character of the IPA-use in conversation. Additionally, we highlight a number of nuanced practicalities of their use in multi-party settings. By providing a depiction of the nature and methodical practice of their use, we are able to contribute our findings to the design of IPAs

Towards the Humanisation of Programming Tool Interactions

Program analysis tools, from simple static semantic analysis by a compiler, to complex dynamic analyses of data flow and security, have become commonplace in modern day programming. Many of the simpler analyses, such as the afore- mentioned compiler checking or linters designed to enforce code style, may even go unnoticed or unconsidered by most users, ubiquitous as they are. Despite this, and despite the obvious utility that such programming tools can provide, many warnings provided by them go unheeded by programmers most of the time.There are several reasons for this phenomenon: the propensity to produce false positives undermines confidence in the validity of warnings, the tools do not in- tegrate well into the normal workflow of the developer, sometimes the warning message is simply too esoteric for most users to understand, and so on. A com- mon theme can be drawn from these reasons for ignoring the often-times very useful information given by a programming tool: the tool itself is difficult to use.In this thesis, we consider ways in which we can bridge this gap between users and tools. To do this, we draw from observations about the way in which we interact with each other in the most basic human-to-human context. Applying these lessons to a human-tool interaction allow us to examine ways in which tools may be deficient, and investigate methods for making the interaction more natural and human-like.We explore this issue by framing the interaction as a "conversation" between a human and their development environment. We then present a new programming tool, Progger, built using design principles driven by the "conversational lens" which we use to look at these interactions. After this, we present a user study using a novel low-cost methodology, aimed at evaluating the efficacy of the Progger tool. From the results of this user study, we present a new, more streamlined version of Progger, and finally investigate the way in which it can be used to direct the users attention when conducting a code comprehension exercise

User Intent Communication in Robot-Assisted Shopping for the Blind

The research reported in this chapter describes our work on robot-assisted shopping for the blind. In our previous research, we developed RoboCart, a robotic shopping cart for the visually impaired (Gharpure, 2008; Kulyukin et al., 2008; Kulyukin et al., 2005). RoboCart's operation includes four steps: 1) the blind shopper (henceforth the shopper) selects

Human or Machine: Reflections on Turing-Inspired Testing for the Everyday

In his seminal paper "Computing Machinery and Intelligence", Alan Turing introduced the "imitation game" as part of exploring the concept of machine intelligence. The Turing Test has since been the subject of much analysis, debate, refinement and extension. Here we sidestep the question of whether a particular machine can be labeled intelligent, or can be said to match human capabilities in a given context. Instead, but inspired by Turing, we draw attention to the seemingly simpler challenge of determining whether one is interacting with a human or with a machine, in the context of everyday life. We are interested in reflecting upon the importance of this Human-or-Machine question and the use one may make of a reliable answer thereto. Whereas Turing's original test is widely considered to be more of a thought experiment, the Human-or-Machine question as discussed here has obvious practical significance. And while the jury is still not in regarding the possibility of machines that can mimic human behavior with high fidelity in everyday contexts, we argue that near-term exploration of the issues raised here can contribute to development methods for computerized systems, and may also improve our understanding of human behavior in general

Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

The design of collaborative projects : Language, metaphor, conversation and the systems approach

This thesis uses a systems approach to develop a model for Collaborative Project Design (CPD). Failure of the software process is the area of concern. The focus of the argument is, however, on the organizational environment of the software process. A central argument is that the analytic tools of standard software development methodologies are inappropriate for systems synthesis. They provide little assistance in coping with the loose complexity that is inherent in the organizational environment in which the software process is embedded. These analytic tools and the engineering language and metaphor which dominate the software process undermine collaboration and disempower business users. CPD was developed to enable viable collaboration that is necessary for the software process to succeed. The purpose of CPD is to provide a systemic model of causal influences and social process in order to guide a project designer when intervening in projects which call for acts of shared creation and/or discovery. CPD was developed through a combination of action research (in projects involving software development and organisational transformation) and theoretical readings focused on the philosophy of meaning, systems thinking, social process and the software process. CPD emphasises that a collaborative project requires careful design of its underlying languages, metaphors and conversations. It identifies three distinct types of conversation, namely communication, dialogue and collaboration. The thesis describes how these conversation types are utilised in transforming a project's network of commitments from loose complexity via shared meaning to cohesive simplicity. Associated with each conversation type is a set of project influences which are developed into a causal influence model in order to depict a collaborative project as a dynamic system of mutually interdependent influences. This causal influence model was used to synthesise the learning from action research and the theoretical readings. An appreciative systems framework was then derived in order to justify a collaborative project as a self-regulating social system and was overlaid onto the causal influence model in order to derive CPD in its final form. CPD proved beneficial when tested in practical projects as a framework to organise a project designer's mind when designing project interventions

A Survey on Large Language Model based Autonomous Agents

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository for the related references at https://github.com/Paitesanshi/LLM-Agent-Survey.Comment: 32 pages, 3 figure