Educational Technology - Artificial Intelligence
A. Mehri; E. Sadipour; F Dortaj
Abstract
Background and Objectives: Artificial intelligence has significantly changed the perspectives and methods of education and learning as a new educational technology. A segment of artificial intelligence includes natural language processors that can produce human-like texts, such as AI chatbots. Due to ...
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Background and Objectives: Artificial intelligence has significantly changed the perspectives and methods of education and learning as a new educational technology. A segment of artificial intelligence includes natural language processors that can produce human-like texts, such as AI chatbots. Due to their abilities to understand human language and answer users' questions, AI chatbots are recognized as important tools for education and learning. However, because the use of AI chatbots in education is still a recent development, there are currently very few studies on the effectiveness of this technology in learning. Accordingly, this research aims to examine the effects of AI chatbot-based blended learning in psychology on self-directed learning, student engagement, and critical thinking disposition among 11th-grade female students in the humanities in Zanjan.
Methods: This research was applied in purpose and quantitative in the nature of the collected data. It employed a quasi-experimental method with a pre-test and post-test design, involving a control group. The statistical population for this study comprised all 11th-grade female students in the humanities in Zanjan. A multi-stage cluster sampling technique was used, wherein one public girl’s high school in Zanjan, specifically Roghani Zanjani School, was randomly selected for the study, and two classes of students were assigned to the experimental and control groups. The sample consisted of 45 participants, divided into 21 in the experimental group and 24 in the control group. Data collection tools included the Self-Directed Learning Questionnaire by Fisher et al. (2001), the Academic Engagement Questionnaire by Reeve and Tseng (2011), and the Critical Thinking Disposition Questionnaire by Ricketts and Rudd (2005). The educational intervention spanned 8 sessions, each 90 minutes long, using the Sider software that offers and supports the ChatGPT AI chatbot. Given the multi-dimensional nature of all three dependent variables and the implementation of pre-tests, Multivariate Analysis of Covariance (MANCOVA) was utilized, and the pre-test and post-test data were analyzed with SPSS version 26.
Findings: The analysis of the data using statistical measures such as mean and standard deviation showed that the mean scores of the post-test for the experimental group increased in comparison to the control group across all three dependent variables when compared to the pre-test. Furthermore, the results from the multivariate analysis of covariance indicated a significant difference in self-directed learning, academic engagement, and critical thinking disposition between the experimental and control groups at the post-test (p < 0.01). Thus, given the higher mean scores of the experimental group in the post-test relative to those in the control group, it can be concluded that the AI chatbot-based blended learning approach in the psychology course proved effective in increasing self-directed learning, academic engagement, and critical thinking among 11th-grade female students in the humanities in Zanjan.
Conclusion: Drawing on the findings from this research, we can conclude that the use of AI chatbots in conjunction with the formal psychology curriculum has great potential to enhance self-directed learning, academic engagement, and critical thinking disposition. However, there is a concern that dependence on the AI chatbot could result in diminished academic engagement, with learners potentially accepting the output data without critical evaluation. Therefore, it is recommended that educators and learners utilize AI chatbots as educational complements and learning aids while considering ethical educational implications, stressing the importance of teacher supervision and instructing adolescents in self-regulation. It should also be noted that this study had limitations, including a lack of resources and prior research, restrictions on access to AI chatbots for IPs within Iran, and various hardware and software challenges in installing the AI chatbot software.
Educational Technology - Artificial Intelligence
M. Rajabiyan Dehzireh
Abstract
Background and Objectives: In recent decades, artificial intelligence has become increasingly prevalent in our lives and has had a significant impact on various fields including education. In the 21st century, education is undergoing a profound transformation, and at the heart of this revolution is artificial ...
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Background and Objectives: In recent decades, artificial intelligence has become increasingly prevalent in our lives and has had a significant impact on various fields including education. In the 21st century, education is undergoing a profound transformation, and at the heart of this revolution is artificial intelligence. AI is reshaping the way we teach and learn, providing numerous opportunities to enhance the learning experience for both teachers and students. AI-powered educational platforms have the ability to collect and analyze vast amounts of data, allowing them to gain insights into students' strengths, weaknesses, and learning preferences. The purpose of this research was to identify the challenges and capabilities of artificial intelligence in teaching and learning and to propose solutions.Methods: A qualitative phenomenological research approach was employed using an exploratory strategy. The study commenced with a systematic review of relevant literature and articles. After a thorough review of the existing research and the identification of initial indicators, interviews were conducted with experts in the field. The interview data were analyzed using thematic analysis. The population of this study included all experts and faculty members in the fields of educational technology and artificial intelligence. A purposeful sampling method was used to select 15 participants, ensuring theoretical saturation. Semi-structured interviews were used as the data collection tool. The data were coded using an interpretive thematic analysis approach. To ensure the reliability and validity of the data, the criteria of reliability and final validity were employed.Findings: Research findings indicated the identification of 112 basic themes, 29 organizing themes, and 3 overarching themes. In this regard, the challenges and issues associated with artificial intelligence included educational, ethical, legal, and security, social and interactive, technological and infrastructural, cultural, and economic challenges. Based on the research findings, the capabilities of research included improving assessment and evaluation processes and providing feedback, ensuring global access and educational equity, enhancing faculty capabilities in the educational process, teaching various disciplines, content production, instructional design, innovation in the educational process, fostering creativity and thinking, making education interactive, education and support for students with special needs, strengthening scientific skills, encouraging learning, innovating in the delivery of educational services, artificial intelligence as a teaching assistant, and adapting education to individual needs using artificial intelligence. Research findings revealed that the solutions to these challenges included using artificial intelligence as a teaching assistant, reviewing and evaluating data generated by artificial intelligence, developing policies, laws, and protocols in the field of artificial intelligence application, producing, building, and designing artificial intelligence applications, interaction between experts in the field of artificial intelligence in education, developing the necessary hardware and software for artificial intelligence applications, improving performance evaluation methods with artificial intelligence, and promoting a culture and education on how to use artificial intelligence.Conclusion: Creating comprehensive AI literacy programs is essential to ensure that learners and educators can navigate the AI landscape effectively. These programs should not only address technical aspects but also data privacy and ethical considerations. By equipping individuals with the necessary knowledge and skills, institutions can promote the ethical use of AI and mitigate potential risks.
Educational Technology - Artificial Intelligence
M. Rezaei; E. Pazouki; R. Ebrahimpour
Abstract
Background and Objectives: Today, due to the increasing development of technology all over the world, e-learning systems are expanding rapidly. With the progress of electronic education, the movement from traditional education (the approach of providing one education for all) to personalized education ...
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Background and Objectives: Today, due to the increasing development of technology all over the world, e-learning systems are expanding rapidly. With the progress of electronic education, the movement from traditional education (the approach of providing one education for all) to personalized education began. Personalized education is an educational approach that aims to customize learning based on a learner's strengths, skills, interests, and needs. This method of education, like any other new method, has its strengths and weaknesses. In fact, increasing motivation and acquiring self-defense skills can be considered as one of the important benefits of this type of training. On the other hand, as the weaknesses of this method, we can mention the time-consuming training, the challenge in implementation, and the lack of clarity in the method of application. Due to the availability of many data from learners, the use of artificial intelligence to personalize education will both increase the quality and make education more attractive. Nowadays, one of the ways to personalize education is to provide it based on the preferences of learners. Learner preferences can be self-identified and explicitly identified and extracted by directly asking the learner or implicitly and collecting and monitoring data. Today, modeling user preferences is one of the most challenging tasks in e-learning systems that deal with a large amount of information. The aim of this research was to extract the implicit preferences of the learner by using an online interactive intelligent educational system that models the learner's preferences using conceptualization for learning objects through profile expansion and the use of artificial intelligence algorithms. The model was trained with the collected interactive data and provides new learning objects based on the learner's preferences. This research was practical in terms of purpose.Methods: In this research, according to the society available to us, 29 male and female undergraduate students of computer sciences, with an average age of 21.5 years, who had not taken the machine learning course, were included as the participants. After registration, the participants were randomly divided into two control and experimental groups. The experimental group was presented with personalized content that matched their preferences, and the control group was presented with content that did not match their preferences. After the training, the learning rate and cognitive load of the participants were measured by the designed performance test and the NASA workload index questionnaire. At the end, the significance level of the obtained results of the two groups was evaluated using the independent t-test.Findings: Based on the obtained results, the average performance test scores of the experimental group who received content matching their preferences had no significant difference compared to the average of the control group with a value of p=0.7 (while learning), but the cognition of the control group was significantly lower with p=0.00 compared to that of the experimental group.Conclusion: Based on the findings of the research, providing personalized educational content based on learners' preferences using the profile expansion technique significantly reduced the cognitive load during learning. So, Providing educational content based on learners' preferences, as one of the personalized educational methods in e-learning, plays an important role in reducing the cognitive load of learners.
Educational Technology - Artificial Intelligence
Vi. Gohari; M. Keramati Nojedeh sadat; F. Ramezaivishki
Abstract
Background and Objectives: Education is one of the main parts of knowledge and science production in which teachers contribute to the scientific and cultural progress of any country. Inefficiency in education and training can lead to challenges and widespread problems of social, cultural, scientific, ...
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Background and Objectives: Education is one of the main parts of knowledge and science production in which teachers contribute to the scientific and cultural progress of any country. Inefficiency in education and training can lead to challenges and widespread problems of social, cultural, scientific, political, religious dimensions in society. Therefore, it seems necessary to assess the competence of student teachers in terms of knowledge, skills, and attitudes especially before entering the professional field of teaching. In this research, the competency of student teachers before entering education and training was evaluated using artificial intelligence neural network as a precise computing tool.Methods: In the present study, the research method is applied and quantitative. The statistical population consisted of students of Farhangian University, and the sample included 91 teacher students who were selected via cluster sampling in 2015-2016 in the field of Biology Education of Shahid Beheshti Higher Education Center of Farhangian University. A corpus of 500 data (80% training and 20% test) was formed based on the scores of general, educational, specialized educational, theoretical, research, internship, and total average courses. The findings were assessed using the feedforward neural network method. According to the investigations carried out on the layers and the number of neurons on the data of the algorithm, a three-layer neural network was designed with two hidden layers with a number of 300 neurons and an output layer with a number of 1 neuron.Findings: The results of the examination of professional competence are based on the scores of student teachers and the extraction of new data of the seven dimensions of theoretic- specialization, education- specialization, education- practice, general, internship, research, and total average, which show the highest average with a score of 19.8 in the educational courses and the lowest average with a score of 16.67 was in theory-specialized courses. The analysis of the findings according to the educational data of the graduates and the labeling of experts showed the level of competence as 17.77.Conclusion: These results indicate that curriculum planners should pay more attention to specialized-theory courses because teachers must have the appropriate and sufficient knowledge and scientific level to present scientific materials to students in addition to learning educational lessons. Besides, it is suggested to evaluate the competence coefficient in other studies by using special questionnaires based on students' attitudes toward the teaching profession. On the other hand, the student's handwriting can be examined and studied as another indicator. Also, the neural network model of artificial intelligence should be used to determine the competence of student teachers in other fields of basic sciences and humanities.