Artificial Intelligence (AI) has gained significant attention in recent years, permeating various sectors and transforming the way tasks are performed. In the field of education, AI has the potential to revolutionize traditional teaching and learning methodologies, particularly in the context of English as a Second Language (ESL) classrooms. This systematic literature review aims to provide a comprehensive overview of the current state of research on the implementation, challenges, and impacts of AI in ESL classrooms based on different countries. For this purpose, a systematic review has been carried out in ERIC, WOS and Scopus databases. After applying the inclusion and exclusion criteria, the sample was set at 25 articles. The findings reveal that AI technologies offer promising opportunities to enhance ESL instruction. Despite the potential benefits, the review also uncovers several challenges and limitations associated with AI implementation in ESL classrooms. Furthermore, the review identifies the need for further empirical research to measure the long-term effects of AI. In conclusion, this systematic literature review provides valuable insights into the current landscape of AI implementation in ESL classrooms. It highlights the potential of AI technologies to enhance language instruction, while acknowledging the challenges that need to be addressed. The findings of this review can guide educators, policymakers, and researchers in making informed decisions about the integration of AI in ESL classrooms, fostering effective and inclusive language learning environments in the digital era besides to require further research and analysis on AI in Malaysian ESL classroom context.

https://hrmars.com/papers_submitted/20422/benefits-and-challenges-in-implementing-artificial-intelligence-in-education-aied-in-esl-classroom-a-systematic-review-2019-2022.pdf

Benefits and Challenges in Implementing Artificial Intelligence in Education (AIED) in ESL Classroom: A Systematic Review (2019-2022)

Using the interactive software FossilSketch to teach micropaleontology to undergraduate students

This full research paper presents a systematic mapping of applications of affective intelligent tutor systems in engineering education. These systems employ techniques such as self-questionnaires, voice or facial recognition, system usage tracking, or combinations of these approaches. The data collection occurs through the sensors that measure input from devices, such as microphones, cameras, and body sensors. This paper presents a systematic mapping of affective intelligent tutoring systems in engineering courses and seeks to answer the following questions: Q1. Which engineering courses employ affective intelligent systems?; Q2. What secondary or auxiliary technologies, if any, do the authors use in combination with affective tutoring systems?; Q3. How do these studies perform emotion recognition?; Q4. What emotions do these studies recognize?; Q5. Which evaluation methods do the studies use? The research concentrated on primary sources, such as articles and conference proceedings published between 2010 and 2022 on established scientific databases, including ACM Digital Library, IEEE Xplore, Science Direct, and Springer. We identified 413 potential articles and narrowed the options down to 23 papers that fully matched the inclusion criteria. We contribute to the literature by answering the proposed questions and categorizing the existing papers to find directions for future research.

A Systematic Mapping of Affective Intelligent Tutor Systems in Engineering Education

It is challenging to effectively educate in large classes with students from a multitude of backgrounds.Many introductory engineering courses in universities have hundreds of students, and some online classes are even larger.Instructors in these circumstances often turn to online homework systems, which help greatly reduce the grading burden; however, they come at the cost of reducing the quality of feedback that students receive.Since online systems typically can only automatically grade multiple choice or numeric answer questions, students predominately do not receive feedback on the critical skill of sketching free-body diagrams (FBD).An online, sketch-recognition based tutoring system called Mechanix requires students to draw free-body diagrams for introductory statics courses in addition to grading their final answers.Students receive feedback about their diagrams that would otherwise be difficult for instructors to provide in large classes.Additionally, Mechanix can grade open-ended truss design problems with an indeterminate number of solutions.Mechanix has been in use for over six semesters at five different universities by over 1000 students to study its effectiveness.Students used Mechanix for one to three homework assignments covering free-body diagrams, static truss analysis, and truss design for an openended problem.Preliminary results suggest the system increases homework engagement and effort for students who are struggling and is as effective as other homework systems for teaching statics.Focus groups showed students enjoyed using Mechanix and that it helped their learning process. 

Changing Homework Achievement with Mechanix Pedagogy: A Recap

Engineering students need practical, open-ended problems to help them build their problem-solving skills and design abilities. However, large class sizes create a grading challenge for instructors as there is simply not enough time nor support to provide adequate feedback on many design problems. In this work, we describe an intelligent user interface to provide automated real-time feedback on hand-drawn free body diagrams that is capable of analyzing the internal forces of a sketched truss to evaluate open-ended design problems. The system is driven by sketch recognition algorithms developed for recognizing trusses and a robust linear algebra approach for analyzing trusses. Students in an introductory statics course were assigned a truss design problem as a homework assignment using either paper or our software. We used conventional content analysis on four focus groups totaling 16 students to identify key aspects of their experiences with the design problem and our software. We found that the software correctly analyzed all student submissions, students enjoyed the problem compared to typical homework assignments, and students found the problem to be good practice. Additionally, students using our software reported less difficulty understanding the problem, and the majority of all students said they would prefer the software approach over pencil and paper. We also evaluated the recognition performance on a set of 3000 sketches resulting in an f-score of 0.997. We manually reviewed the submitted student work which showed the handful of student complaints about recognition were largely due to user error.

An Intelligent System to Analyze Sketched Solutions to Open-Ended Truss Problems

Providing opportunities for students to exercise their creative skills in large, entry engineering classes challenges most faculty. This paper presents a study of a large statics class provided with a homework problem that asks them to design a truss structure. Automatic grading was done by Mechanix, an AI tutor-based software package that can automatically recognize a free-body diagram or a planar, 2d, statically determinate truss structure. The paper presents a study done in two different semesters, comparing the students using Mechanix to a control (problem on paper). To ease grading, the control group's trusses were analyzed by Mechanix after submission. No mean homework grade differences were observed, but students in the Mechanix group produced trusses that could withstand higher loads. This is despite the fact the only guidance or feedback Mechanix provides was if the students' calculated max load was correct, and if it was not, which member failed. This study occurred in Fall 2019 and Spring 2020. Students also submitted more attempts in Mechanix than the control. It may be students in the control group only submitted correct answers despite being asked to submit all attempts. Future work will provide more incentive for students to submit all attempts on paper. Mechanix automatically records all attempts. During high stress (Covid-19), more students in the Mechanix group submitted the assignment indicating that students may find this system less mentally taxing to use, less stressful, or something else led to this difference. It will be explored with focus groups in the future. AI tools have the potential to provide automatic grading for open-ended, creativity required, design problems, and to engage students more, allowing universities to develop more innovative engineers while also deepening their knowledge.

A Study on the Impact of a Statics Sketch-Based Tutoring System Through a Truss Design Problem

Large class sizes in engineering programs often prevent instructors from providing detailed and meaningful feedback to students on their homework problems. While the literature shows that frequent and immediate formative feedback has several benefits in terms of knowledge gain and academic motivation, several instructors struggle to provide any feedback. Motivated by this inability, a sketch-based virtual tutoring system, named Mechanix, has been developed and implemented. Mechanix lets the students to sketch their freebody diagram on a virtual interface and the process involved is very close to using a pencil and paper. The system provides real-time feedback on the accuracy of their Freebody diagrams and the solution to the problem. This paper reports the implementation of Mechanix at two large public universities in the United States – Georgia Institute of Technology and Texas State University. Mechanix is used to solve specific assignments from each school that involve the use of freebody diagrams. Preand postconcept inventories are used to measure the improvements in the conceptual understanding of the students. The results show that students who solve their homework using Mechanix outperform their peers who do not in one school, whereas the results are similar across the two groups in the second school. The evaluation of the concept inventories shows that the students who used Mechanix has the same level of improvement in their conceptual knowledge compared to the control group.

Impact of a Sketch-based Tutoring System at Multiple Universities

Large class sizes in engineering programs often prevent instructors from providing detailed and meaningful feedback to students on their homework problems. While the literature shows that frequent and immediate formative feedback has several benefits in terms of knowledge gain and academic motivation, several instructors struggle to provide any feedback. Motivated by this inability, a sketch-based virtual tutoring system, named Mechanix, has been developed and implemented. Mechanix lets the students to sketch their freebody diagram on a virtual interface and the process involved is very close to using a pencil and paper. The system provides real-time feedback on the accuracy of their Freebody diagrams and the solution to the problem. This paper reports the implementation of Mechanix at two large public universities in the United States – Georgia Institute of Technology and Texas State University. Mechanix is used to solve specific assignments from each school that involve the use of freebody diagrams. Pre- and post- concept inventories are used to measure the improvements in the conceptual understanding of the students. The results show that students who solve their homework using Mechanix outperform their peers who do not in one school, whereas the results are similar across the two groups in the second school. The evaluation of the concept inventories shows that the students who used Mechanix has the same level of improvement in their conceptual knowledge compared to the control group.

/pdf/a-study-on-the-impact-of-a-sketch-based-tutoring-system-in-2j59ftqxrw.pdf

A Study on the Impact of a Sketch-based Tutoring System in Statics Instruction

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A Study on the Impact of a Statics Sketch-Based Tutoring System Through a Truss Design Problem

Impact of a Sketch-based Tutoring System at Multiple Universities

A Study on the Impact of a Sketch-based Tutoring System in Statics Instruction

An Intelligent System to Analyze Sketched Solutions to Open-Ended Truss Problems