Intelligence Course Research Articles

Innovation and Practice on the Course of Artificial Intelligence

Innovation and Practice on the Course of Artificial Intelligence

Impact of basic artificial intelligence (AI) course on understanding concepts, literacy, and empowerment in the field of AI among students

AbstractWith the development of information technologies and information processing methods, it is important to provide high‐quality education in the field of artificial intelligence (AI). The study aims to investigate the impact of an educational course on AI on the comprehension of concepts, literacy, and empowerment in the field of AI among students of higher educational institutions. The experiment involved 125 students from Hohai University in China. As a result of taking the training course, students were able to improve their understanding of concepts (increasing their average score from 6.33 to 9.69), literacy (from 2.94 to 3.99), and empowerment (from 3.90 to 4.04) in AI. The resulting data statistically confirmed the effectiveness of the developed course for improving confidence in the field of AI. The training module can be applied to improve confidence in the field of AI for students in various careers, as information competence is important these days and increases the success of graduates in employment. When it comes to further research, the encouraging results of this study suggest opportunities for promoting this training program among a diverse group of participants. To confirm the effectiveness of the developed course, it can be conducted among students in schools and other educational institutions, reducing it to even more basic if necessary.

Research on the Application Status of Generative AI Tools among College Students--Taking ChatGPT as an Example

In recent years, artificial intelligence has continuously overcome technological challenges and swept the world at a rapid pace of development. Generative AI tools represented by ChatGPT have undergone extensive language training and made a qualitative leap in capacity algorithms. Although the application status of ChatGPT has received widespread attention, there is still a limited amount of related research and shortcomings. This article takes ChatGPT as an example to analyze the current application status of generative AI tools among college students. Through the collection and organization of survey data, research, and analysis have found that college students have some problems with the actual use of ChatGPT, including insufficient familiarity, inconvenient use of ChatGPT, and lack of autonomy in using ChatGPT among college students. Based on this, this article proposes the following suggestions. Firstly, draw on mature experience and promote digital education. Secondly, offer artificial intelligence courses such as ChatGPT pilot courses. Thirdly, build and improve the digital moral education system and regulatory mechanism.

A PERSPECTIVE INTO THE FUTURE OF TEACHING AND LEARNING IN THE CONTEXT OF THE RISING INTEREST IN ARTIFICIAL INTELLIGENCE IN EDUCATION. OPPORTUNITIES AND ETHICAL CHALLENGES

: The paper aims to provide an image of the future of teaching and learning in the context of artificial intelligence transforming various industries, including sports, education, and construction. Its place in education is a frequently discussed topic. While some argue that artificial intelligence will revolutionize education, others worry that it will take over to the harm of educators and students. Though robotics in the classroom is still a ways off, artificial intelligence is finding its way into the classroom. AI has the power to improve teaching and learning methods, solve some of the largest issues facing education today, and hasten the achievement of inclusive and equitable quality education. In addition to delivering artificial intelligence courses, EdTech businesses are increasingly using eLearning solutions to personalize learning experiences, pinpoint knowledge gaps, and give focused feedback. Also, AI-driven education is upending conventional teaching methods and influencing how this field will use technology in the future. With the use of complex algorithms and massive data sets, artificial intelligence solutions for education may provide a lot of advantages, yet as with the use of artificial intelligence in any context, there are significant ethical considerations which are a hot topic of discussion in the technology world and beyond, and the majority of university degree programs are including courses on AI ethics in their curricula. Therefore, the paper presents the benefits of AI in the classroom, such as engagement and assistance for students, assessment and evaluation, and individualized learning but also about the difficulties and worries associated with AI in education, including prejudice and privacy issues, as well as the moral issues raised by AI-powered learning. It also discusses the possible effects on the educational system and how students are trained for the workforce of the future as potential applications of AI in education are explored.

과정 중심 평가가 반영된 인공지능 기초 수업이 고등학생의 융합적 사고력, 협력적 문제 해결력에 미치는 효과

Objectives This study analyzes the effects of an artificial intelligence basic course with process-focused assessment on high school students' integrative thinking and collaborative problem-solving abilities. Methods The study was conducted with 63 high school students in Seoul as the control group and 61 as the experimental group. The experimental group took classes incorporating process-focused assessment for the ‘Data and Machine Learning’ unit of the basic artificial intelligence course, while the control group took classes using the existing textbook for the same unit. For the process-focused assessment classes, 7 lessons were developed based on the backward design model, and the validity of the lesson content and measurement tools was verified through a Delphi survey with 13 experts. Pre and post-tests were conducted to measure integrative thinking and collaborative problem-solving abilities, and t-tests were performed for statistical analysis of the test results. Additionally, interviews with students who participated in each class and their performance assessment results were qualitatively analyzed. Results Analysis of the pre and post-test results showed that both groups showed statistically significant differences in all areas of integrative thinking and collaborative problem-solving abilities. However, compared to the existing classes, the process-focused assessment classes showed statistically significant improvements in the overall integrative thinking area and the collaborative problem-solving areas of joint problem analysis and definition, solution implementation, and evaluation and reflection. Furthermore, the qualitative analysis revealed that the experimental group outperformed the control group in terms of the quality and quantity of student feedback and task performance results. Conclusions The artificial intelligence basic course incorporating process-focused assessment improved high school students' integrative thinking and collaborative problem-solving abilities. The backward design model is an appropriate teaching method for implementing process-focused assessment, and further research is needed on an instructional model that includes steps and procedures for process-focused assessment classes that teachers can easily reference based on this study.

Perceptions and Aspirations of Undergraduate Computer Science Students Towards Generative AI: A Qualitative Inquiry

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, perhaps even those not directly covered in their curriculum, piqued their interest. Furthermore, the dialogue encompassed the learning methodologies highlighted in the textbook, seeking students’ feedback on the strategies that have been most effective for mastering complex AI theories and their practical applications. Interdisciplinary applications of AI were also discussed, encouraging students to contemplate AI’s role beyond the realm of computer science and its potential to foster innovative solutions across various fields. Finally, the conversation shifted towards students’ personal goals and aspirations in AI, urging them to consider how their perspectives have evolved in light of technological advancements, societal needs, and ethical considerations. The findings underscore a notable gap in students’ awareness of AI’s history and its current capabilities, indicating a need for educational strategies that not only deepen understanding but also foster a broader appreciation of AI’s potential. This study contributes valuable insights into enhancing AI education and encouraging interdisciplinary innovation among the next generation of computer scientists.

An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model

Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students’ engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM) to incorporate cognitive factors such as AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), alongside human–computer interaction (HCI) elements like user interface (UI), content (C), and learner-interface interactivity (LINT) in the context of using generative AI (GenAI) tools. By including these factors, an expanded model is presented to capture the complexity of student engagement with AI education. To validate the model, 210 Chinese students spanning grades K7 to K9 participated in a 1 month artificial intelligence course. Survey data and structural equation modeling reveal significant relationships between cognitive and HCI factors and perceived usefulness (PU) and ease of use (PEOU). Specifically, AIIM, AIRD, AICF, UI, C, and LINT positively influence PU and PEOU, while AIAX negatively affects both. Furthermore, PU and PEOU significantly predict students’ attitudes toward AI curriculum learning. These findings underscore the importance of considering cognitive and HCI factors in the design and implementation of AI education initiatives. By providing a theoretical foundation and practical insights, this study informs curriculum development and aids educational institutions and businesses in evaluating and optimizing AI4K12 curriculum design and implementation strategies.

Fuzzy Neural Network Model for Intelligent Course Development in Music and Dance Education

Interactions are mandatory for online or offline music and dance education to improve understandability and learning efficacy. The course designed for such artistic education incorporates multi-point interactions and monotonous presentations. The validation of the key factor: interactivity is thus mandatory for enhancing efficiency. This article introduces an interactivity validation method (IVM) using combined fuzzy neural network (FNN) to aid artistic course development. The output of the existing course and its evaluation criteria are considered in enhancing its grade. The fuzzy performs interaction classification as mandatory and trivial based on the student’s performance. The neural network identifies the chances for maximum performance by increasing or decreasing the interaction rate. If a saturated performance is achieved at a high or low interactivity, then the further course design is performed based on the saturated interactivity factor. The failing factors are used for training the neural network for modifying the interactivity rate from the current course development suggestion. Such a process is keen on classifying and validating the impact of interactivity over artistic course design.

Вплив соціального інтелекту на продуктивність перебування людини у соціальних медіа

Given the ambiguity of the impact of social media on individuals, it is crucial to find effective regulatory mechanisms that allow for the successful utilization of this resource in adapting to the changing conditions of today. The article analyzes the influence of a person’s social intelligence on their inclination towards the productive use of modern digital technologies, particularly social media. The study examined the average time individuals spend on social media, the percentage of platform usage for work-related tasks, and the desire to further develop digital skills. Surveys were conducted among representatives of local government, occupying various positions from specialists to department heads. The TROMSØ express survey method was used to determine the level of social intelligence among respondents. The impact of social intelligence on the productivity of individuals’ presence on social media was assessed using the Spearman’s rank correlation coefficient. In total, 153 individuals with an average age of 43 participated in the study. The majority of respondents were female, accounting for 80 % of the sample. A high level of social intelligence development was identified within the sample. Regarding differences between females and males, no significant distinctions were found, nor were differences detected in the amount of time spent on social media and the desire to attend thematic courses on using this informational resource. It was established that social intelligence significantly influences (p≤0.001***) an individual’s inclination towards productive use of social media, which is associated (p≤0.05*) with the desire for additional development of communication skills in the online space. People with a higher level of social intelligence can understand the needs and expectations of their interlocutors better, which will help build fruitful communication and resolve conflict situations. They are also better at recognising manipulative tactics online, which helps them avoid negative influences and maintain healthy relationships with other users. This underscores the necessity of implementing social intelligence courses into higher education curricula and the feasibility of introducing similar training sessions, lectures, and workshops to enhance the qualifications of local government representatives, universities, and businesses.

Research on Curriculum Construction of Artificial Intelligence Under the Background of New Engineering

Under the background of new engineering, artificial intelligence courses in applied undergraduate colleges and universities face great challenges in course teaching and students’ learning effect due to the comprehensive factors such as content, class hours, and students’ knowledge structure. In order to improve the level and quality of talent training, this paper explores and practices the problems existing in the teaching of artificial intelligence courses in applied undergraduate colleges from the aspects of curriculum teaching objectives, contents, methods, teacher construction, and experimental teaching, promoting learning through competition and teaching assessment, and insists on taking “applied and innovative” learning training as the center. It is necessary to constantly optimize the ideas and methods of artificial intelligence course construction, and promote the development and innovation of computer education in application-oriented colleges under the background of new engineering.

IMPLEMENTASI CLUSTERISASI UNTUK PENGELOMPOKKAN GAYA BELAJAR MAHASISWA DENGAN METODE K MODES

One of the keys to success in learning is determining student learning styles. Learning styles are grouped into 3, grouping learning styles based on the characteristics of students. Sample data was used for 3 classes in the artificial intelligence course with total data of 83 students who answered 36 questions. To be able to carry out student mapping using the k modes method for clustering. The K modes method is used because the data used is categorical. K modes can be used for multi-dimensional clustering and shorter computing times. With the clustering application for grouping student learning styles with a sample of 83 students by answering 36 questions to be divided into 3 groups, the results were 37 students for the visual group, 31 students for the auditory group, and 15 students for kinesthetics. At the testing stage, black box testing is used. By knowing learning style groups, students can easily learn and absorb information.

Research on Intelligent Courses in English Education based on Neural Networks

Research on Intelligent Courses in English Education based on Neural Networks

Implementation of an Online Programming Software to Explore the Computational Thinking Ability of Biological Education Students

This study aimed to explore the Computational Thinking (CT) ability of biology education students in artificial intelligence courses through code.org programming, an online programming software. Students were required to solve problems systematically and design systems with coding for beginners. The research method used a qualitative approach because it was based on observational data from computational media produced in accordance with artificial intelligence learning for education and based on CT indicators. The subjects of this study were 73 students of the Biology Education Study Program in 2021. How students responded to using the code.org platform was captured through a questionnaire distributed via a Google form. The results showed that students could follow each stage of educational game projects through the code.org platform to practice computational thinking skills in accordance with the success of the stages of doing simple projects through good, systematic and problem-solving coding. Student responses to the code.org platform as an online program tool that can hone computational thinking can provide creativity and enthusiasm for student learning. As evidenced by the questionnaire results, 63.3% of students strongly agree that this increases creativity.
 Keywords: online programming, computational thinking, biological education, students

Intelligent recommendation system for College English courses based on graph convolutional networks

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

The Development and Validation of the Learning Engagement Scale for High School Students' Artificial Intelligence Courses

The Development and Validation of the Learning Engagement Scale for High School Students' Artificial Intelligence Courses

Thoughts on the Teaching Reformation of Artificial Intelligence Courses

Thoughts on the Teaching Reformation of Artificial Intelligence Courses

Intelligent English Teaching Based on the Pedagogy of Performing Another Culture and ChatGPT Technology

In the background of digital education, the intelligent capabilities of ChatGPT-based technology in teaching have emerged as a powerful tool for facilitating teachers’ preparation for lesson, improving students’ learning motivation and effectiveness, and promoting the development of intelligent curriculum. This study put forward a novel model of ChatGPT-based English teaching grounded in the “Pedagogy of Performing Another Culture” proposed by Galal Walker. From the three aspects of teaching design, application scenarios, and learning effect evaluation, this model explores the application of ChatGPT in the four fundamental language domains of listening, speaking, reading, and writing, with the objective of enhancing students’ language proficiency and cultural sensitivity, while also fostering their digital learning capabilities. To uphold academic integrity and ethical use of ChatGPT in English teaching, a C++-based ChatGPT application has been designed and tailored to the specific needs of English language learners. The school-based intelligent courses based on ChatGPT are available for students to carry out more diverse and effective English learning, promoting the rational use of ChatGPT in learning.

Practical Teaching and Intellectualization of University Courses Based on the Maximum Information Entropy Model

Abstract Traditional teaching has become a thing of the past, the integration of technology and the classroom has brought about significant changes in education, and the role of information entropy in teaching has gradually manifested itself. In this paper, the behavioral time occupancy of the teaching process of university courses is viewed as a probability distribution event combined with the Lagrange multiplier method to solve the maximum entropy value in the distribution of practical teaching behavior. The practice depth value of classroom behavior can be calculated using the interaction behavior coefficient and interaction behavior entropy. Apply the information entropy to the practical teaching analysis of university courses, verify the practical effect of university courses from different sides, and carry out intelligent course construction according to the current situation of students’ learning behavior. The results show that the average score of class 3 in the practical analysis ability assessment is 4.021, which indicates that the practical analysis ability of students in the class meets the standard, and the students’ practical execution should be mainly cultivated in the later practical teaching. 67.1% of students believe that the intelligent classroom has improved their performance over the traditional classroom according to the intelligent course model.

Exploring the impact of AI-driven digital and intelligent courses on the reform of teaching models for postgraduate students

Exploring the impact of AI-driven digital and intelligent courses on the reform of teaching models for postgraduate students

Research on the Teaching Reform of Intelligent Courses in Military Academy

Research on the Teaching Reform of Intelligent Courses in Military Academy