Bringing AI into the Classroom: Designing Smart Personal Assistants as Learning Tutors

Despite a growing body of research about the design and use of Smart Personal Assistants like Amazon’s Alexa, little is known about their ability to be learning tutors. New emerging ecosystems empower educators to develop their own agents without deep technical knowledge. The objective of our study is to find and validate a general set of design principles that educators can use to design their own agents. Using a design science research approach, we present requirements from students and educators as well as IS and education theory. Next, we formulate design principles and evaluate them with a focus group before we instantiate our artifact in an everyday learning environment. The findings of this short paper indicate that the design principles and corresponding artifact are able to significantly improve learning outcomes. The completed work aims to develop a nascent design theory for designing Smart Personal Assistants as learning tutors

Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Children's interactions with interactive toy technology

Abstract Digital toys offer the opportunity to explore software scaffolding through tangible interfaces that are not bound to the desktop computer. This paper describes the empirical work completed by the CACHET (Computers and Children's Electronic Toys) project team investigating young children's use of interactive toy technology. The interactive toys in question are plush and cuddly cartoon characters with embedded sensors that can be squeezed to evoke spoken feedback from the toy. In addition to playing with the toy as it stands, the toy can be linked to a desktop PC with compatible software using a wireless radio connection. Once this connection is made the toy offers hints and tips to the children as they play with the accompanying software games. If the toy is absent, the same hints and tips are available through an on-screen animated icon of the toy's cartoon character. The toys as they stand are not impressive as collaborative learning partners, as their help repertoire is inadequate and even inappropriate. However, the technology has potential: children can master the multiple interfaces of toy and screen and, when the task requires it and the help provided is appropriate, they will both seek and use it. In particular, the cuddly interface experience can offer an advantage and the potential for fun interfaces that might address both the affective and the effective dimensions of learners' interactions

Towards Empowering Educators to Create their own Smart Personal Assistants

Despite a growing body of research about the design and use of Smart Personal Assistants such as Amazon’s Alexa or Google’s Assistant, little is known about their ability to help educators offering individual support in large-scale learning environments. Smart Personal Assistant ecosystems empower educators to develop their own agents without deep technological knowledge. The objective of this paper is to design and validate a method that helps educators to create Smart Personal Assistants as learning tutors. Using a design science research approach, we first gather requirements from students and educators as well as from information systems and education theory. Next, we create an alpha version of our method and evaluate it with a focus group before we instantiate our artifact in an everyday learning environment. The findings indicate that our method is able to empower educators to design Smart Personal Assistants that significantly improve students’ learning success

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Knowledge Elicitation Methods for Affect Modelling in Education

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy

Building Artificially Intelligent Learning Games

The idea of digital game-based learning (DGBL) is gaining acceptance among researchers, game designers, educators, parents, and students alike. Building new educational games that meet educational goals without sacrificing what makes games engaging remains largely unrealized, however. If we are to build the next generation of learning games, we must recognize that while digital games might be new, the theory and technologies we need to create DGBL has been evolving in multiple disciplines for the last 30 years. This chapter will describe an approach, based on theories and technologies in education, instructional design, artificial intelligence, and cognitive psychology, that will help us build intelligent learning games (ILGs)