Notes on Artificial Intelligence

From leveraging insights in data-driven marketing, to utilizing machine-learning algorithms for medicine, artificial intelligence has been seamlessly integrated in industry to optimize professional performance. While AI technologies attract their fair share of critics, their prevalence in the public domain attests to their profound potential, both as a tool for corporate transformation, and, more recently, as a means to enhance current, pedagogical practice. These notes explore coverage in the current literature regarding both concerns related to and the potential value of integrating AI technologies into the classroom to customize the learning experience through data-driven insights, to facilitate a more efficient allocation of resources, and assist educators in the critical appraisal of their pedagogical approach in order to assess its current efficiency

NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names

Addressing students by their names helps a teacher to start building rapport with students and thus facilitates their classroom participation. However, this basic yet effective skill has become rather challenging for university lecturers, who have to handle large-sized (sometimes exceeding 100) groups in their daily teaching. To enhance lecturers' competence in delivering interpersonal interaction, we developed NaMemo, a real-time name-indicating system based on a dedicated face-recognition pipeline. This paper presents the system design, the pilot feasibility test, and our plan for the following study, which aims to evaluate NaMemo's impacts on learning and teaching, as well as to probe design implications including privacy considerations.Comment: DIS '20 Companio

Social behavioral sensing: an exploratory study to assess learning motivation and perceived relatedness of university students using mobile sensing

Learning motivation plays a crucial role in student’s daily study life since it greatly affects academic performance and engagement. Perceived relatedness, based on self-determined theory, is an important predictor of learning motivation. Today, assessment for both of them still relies on subjective evaluations and self-reports, which is time-consuming and onerous. Hence, we propose a novel approach blended with mobile sensing by simultaneously collecting psychological measurements and objective mobile sensing data from N=58 undergraduates to explore new methods of assessing learning motivation and perceived relatedness. We identify a variety of social behavioral patterns from mobile sensing data, and investigate associations between psychological measures and these patterns. Our study helps enlighten what the new forms of assessing learning motivation and perceived relatedness in education could be, and paves the way for personalizing intervention in future research.This research was supported by the National Natural Science Foundation of China (No. 62077027), and the Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 20YJC190034)

Investigating Students' Experiences with Collaboration Analytics for Remote Group Meetings.

Remote meetings have become the norm for most students learning synchronously at a distance during the ongoing coronavirus pandemic. This has motivated the use of artificial intelligence in education (AIED) solutions to support the teaching and learning practice in these settings. However, the use of such solutions requires new research particularly with regards to the human factors that ultimately shape the future design and implementations. In this paper, we build on the emerging literature on human-centred AIED and explore students’ experiences after interacting with a tool that monitors their collaboration in remote meetings (i.e., using Zoom) during 10 weeks. Using the social translucence framework, we probed into the feedback provided by twenty students regarding the design and implementation requirements of the system after their exposure to the tool in their course. The results revealed valuable insights in terms of visibility (what should be made visible to students via the system), awareness (how can this information increase students’ understanding of collaboration performance), and accountability (to what extent students take responsibility of changing their behaviours based on the system’s feedback); as well as the ethical and privacy aspects related to the use of collaboration analytics tools in remote meetings. This study provides key suggestions for the future design and implementations of AIED systems for remote meetings in educational settings

Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

Understanding the Role of Generative Pre-Trained Transformer (GPT) in Improving Learning Quality and Practices

Generative Pre-trained Transformers (GPTs) are an artificial intelligence model gaining popularity in educational technology development. GPTs are models that are massively trained on diverse texts and can generate texts with structure and meaning. The utilization of GPT in education offers great potential to improve the quality of learning, both inside and outside the classroom. This study aims to understand the role of GPT in improving the quality and practice of learning. This research uses a qualitative research method with a case study method. Data collection techniques in this research include literature study, interviews, and observation. Thematic analysis will be used as the main data analysis technique. The results show that GPT has the potential to be a supportive and interactive tool to increase student motivation in the learning process. The participants perceived GPT as a convenient and efficient means to access information, complete assignments, and receive personalized content tailored to their interests and learning styles

Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling

ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture