Affective learning: improving engagement and enhancing learning with affect-aware feedback

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning

Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop

Although information workers may complain about meetings, they are an essential part of their work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant who executes them as unstructured macrotasks. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.Comment: 10 page

Pro-active Meeting Assistants : Attention Please!

This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all

A first approach to understanding and measuring naturalness in driver-car interaction

With technology changing the nature of the driving task, qualitative methods can help designers understand and measure driver-car interaction naturalness. Fifteen drivers were interviewed at length in their own parked cars using ethnographically-inspired questions probing issues of interaction salience, expectation, feelings, desires and meanings. Thematic analysis and content analysis found five distinct components relating to 'rich physical' aspects of natural feeling interaction typified by richer physical, analogue, tactile styles of interaction and control. Further components relate to humanlike, intelligent, assistive, socially-aware 'perceived behaviours' of the car. The advantages and challenges of a naturalness-based approach are discussed and ten cognitive component constructs of driver-car naturalness are proposed. These may eventually be applied as a checklist in automotive interaction design.This research was fully funded by a research grant from Jaguar Land Rover, and partially funded by project n.220050/F11 granted by Research Council of Norway

How language of interaction affects the user perception of a robot

Spoken language is the most natural way for a human to communicate with a robot. It may seem intuitive that a robot should communicate with users in their native language. However, it is not clear if a user's perception of a robot is affected by the language of interaction. We investigated this question by conducting a study with twenty-three native Czech participants who were also fluent in English. The participants were tasked with instructing the Pepper robot on where to place objects on a shelf. The robot was controlled remotely using the Wizard-of-Oz technique. We collected data through questionnaires, video recordings, and a post-experiment feedback session. The results of our experiment show that people perceive an English-speaking robot as more intelligent than a Czech-speaking robot (z = 18.00, p-value = 0.02). This finding highlights the influence of language on human-robot interaction. Furthermore, we discuss the feedback obtained from the participants via the post-experiment sessions and its implications for HRI design.Comment: ICSR 202

End-User Development for Artificial Intelligence: A Systematic Literature Review

In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.Comment: This version did not undergo peer-review. A corrected version is published by Springer Nature in the Proceedings of 9th International Syposium on End-User Development (ISEUD 2023). DOI: https://doi.org/10.1007/978-3-031-34433-6_