Technology integration is becoming a norm in the education industry, with Al gaining increasing popularity to help improve academic productivity and efficiency. This study proposes to take a different perspective of technology adoption by examining resistance to the use of Al by higher education teachers, despite the tools' and applications' known advantages. The study involves the use of an extended innovation resistance theory (IRT) to achieve the research objective of identifying different functional and psychological barriers, as well as technological and self- inefficacy barriers to Al adoption in instructional design. Moreover, the extent of innovation resistance will be further determined based on the use of Al technologies in various instructional design phases based on the ADDIE Model and expected teaching practitioners' skills and competencies based on the SEIA 8 standards. A mixed-method approach will be used to collect and analyze both quantitative data about Al utilization and qualitative data to determine the barriers, including why such exist. The output of this research aims to help identify opportunities for enhancing teacher competency in technology integration in higher education, Al advocacy, and policy development on the responsible and ethical use of Al in instructional design.

Abstract: This research study employs learning analytics to analyze data and explore the utilization patterns of AnimoSpace, an online learning environment, and its integration by faculty before and during the pandemic. The study provides valuable insights into optimizing learning outcomes and enhancing pedagogical strategies by examining access frequency, application diversity, and technology integration among tertiary educators across eight terms. A significant increase in AnimoSpace usage during the pandemic, particularly from the second term of the Academic Year 2019- 2020 to the third term of the Academic Year 2020-2021 , indicates its effectiveness in supporting online learning and facilitating technology integration among educators. The study also identifies the most frequently used features of AnimoSpace, offering insights for developers and administrators for platform enhancements to improve the online learning experience and support faculty teaching practices. The study's focus on ethical considerations and transparency in learning analytics reassures the audience about the responsible use of technology in education, ensuring that these technologies promote ethical practices and literacy. This research highlights the pivotal role of platforms like AnimoSpace in advancing educational technology, reinforcing the importance of continued research in this area.

Public speaking anxiety (PSA) can hinder academic performance when students face challenges engaging in class discussions, delivering fluent presentations, and undertaking oral examinations. A primary reason for the prevalence of PSA is students' perceived lack of speaking skills. Speech coaching has been found to be an effective way of enhancing public speaking skills but its effects on PSA reduction need further investigation. In this paper, we report our experiments to evaluate the impact of utilizing an AI speech coach in reducing students' anxiety, improving their self-perceived competence, and increasing performance in public speaking tasks. A mixed-method approach combining qualitative analysis of participants' experiences with the AI speech coach and quantitative measures of anxiety levels and speaking performance improvements was utilized. Results from participants' pre- and post- public speaking anxiety scores showed an average of 25.2% reduction in the anxiety levels and 60.5% increase in speaker speech competency. Statistical metrics and personalized feedback given by the speech coach allow participants to monitor their speech and identify areas for improvement which are essential for self-improvement efforts. These findings can inform educators and students on the potential use of digital speech coaches to address PSA while providing the latter with a personalized platform for practicing their speech delivery.

In this paper, we present the design of a dialogue-based tutoring system called DT4-Coding to help novice programmers understand their misconceptions about programming concepts and work towards correcting them. The error or misconception detection and correction are based on Ohlsson's Theory of Learning from Performance Error, which suggests that a mentor (in this case, a software tutor) is necessary to help learners detect these errors and provide feedback to repair faulty knowledge. We utilized the constraint-based model (CBM) for domain and knowledge modeling, which allows human experts to represent knowledge as a constraint. These constraints were used to assess student answers and to generate LLM-based feedback via a question- answer form of dialogue. By incorporating human-written domain knowledge, the system could potentially reduce the problem of LLMs in generating solutions that may be factually incorrect.

This study examines how generative artificial intelligence (GAI) tools, such as ChatGPT, Rytr, and Grammarly, affect education. It uses the Expectation- Confirmation Model (ECM) to analyze four main factors: Expectation Confirmation, Perceived Usefulness, Satisfaction, and Information Systems Continuance. Additionally, two more factors—Utilization and Self-Efficacy—were included. Researchers conducted a survey and analyzed the responses from 31 faculty members and administrative staff. The findings show that Expectation Confirmation and Perceived Usefulness significantly influence user satisfaction and the ongoing use of GAI tools. However, satisfaction alone does not guarantee long-term use of these technologies. Educators generally believe that GAI can improve learning outcomes, and most participants want to keep using these tools. The study notes its limitations, as it mainly focuses on quantitative data and a specific educational environment.

Conversational agents’ ability to communicate in natural language through voice and text interfaces poses an opportunity in helping children with autism spectrum disorder (ASD) develop their social communication skills. In this paper, we describe the design of Amy, a conversational agent that explains social situations to help guide a child in understanding when to use socially appropriate behavior. A breathing exercise feature for emotion regulation is activated when a negative emotion is detected from the child’s input. Interviews with parents and a child psychologist informed the design of Amy as well as the 12 social stories themes that Amy shares with children. Interview inputs and previous works suggested four design considerations for social-emotional learning companions to facilitate better interaction by including relevant social story themes, formulating open-domain conversation flow, incorporating appropriate and guided emotion regulation exercise, and using lively visual user interface.

Storytelling can develop children's emotional intelligence when they are asked to freely talk about their emotions. While parents are responsible for teaching emotional intelligence, studies in using affective technologies to help people become aware of their emotions have also been explored. In this paper, we investigate the opportunity of this technology in enabling children to recognize and express their emotions. We describe a chatbot that leverages storytelling strategies to listen to children as they share emotional events they experienced, then guides them through reflective discipline to devise the next course of action. We report the types of emotions children choose to share with the chatbot, the kinds of support that the chatbot provided, the challenges during the conversation and children's perception of the chatbot. From our findings, we suggest design considerations for a conversation flow that anchors on storytelling to support child-agent interaction.