ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: ADVANCING TEACHING AND LEARNING THROUGH PEDAGOGICAL INTELLIGENCE

Abstract

The method for the development of examining the target audience for the AIEd, 72% of the studies looked at students, 17% at teachers, and 11% at managers. Grounded coding was used to address the main query of how AIEd was used in HE. The data revealed five usage codes: Managing Student Learning, AI Assistant, Predicting, Assessment/Evaluation, and Intelligent Tutoring System (ITS). The results indicate that by implementing AI technology, educational institutions can open up new avenues for effectiveness, accessibility, and customized instruction; however, they also underscore the necessity of addressing ethical issues and implementation challenges. The development of lifelong learning skills, the enhancement of rich teaching abilities, the continuous accumulation of experience, the active improvement and trimming of pertinent change programs, and the collaborative promotion of the rapid development of higher education under artificial intelligence are all embodied in active learning and research on artificial intelligence.

Specification

Description:ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: ADVANCING TEACHING AND LEARNING THROUGH PEDAGOGICAL INTELLIGENCE

Technical Field
[0001] The embodiments herein generally relate to a method for artificial intelligence in higher education: advancing teaching and learning through pedagogical intelligence.
Description of the Related Art
[0002] The citizens of the twenty-first century now live with artificial intelligence everywhere they look, and it is being touted as a tool that can improve and progress every aspect of our lives. Since HE is heavily impacted by the advancement of information and communication technologies, the use of AI has generated a lot of interest. Traditional educational systems have undergone revolutionary changes as a result of the introduction of Google's Gemini and OpenAI's ChatGPT in academia. These artificial intelligence (AI) technologies have quickly gained popularity and have the potential to spark more significant changes in higher education in the future. Academics and research are the most important aspects of conventional higher education that are impacted by AI technology. Higher education's growth offers a pool of talent for China's modernization efforts. Given the speed at which artificial intelligence is developing, higher education instruction needs to be strengthened, new teaching techniques should be investigated, and the revolutionary impact of AI on educational advancement must be recognized through teaching.
[0003] AI capabilities have advanced significantly, the definition of AI has expanded and evolved. "Computing systems that can engage in human-like processes such as learning, adapting, synthesizing, self-correcting, and the use of data for complex processing tasks" is the current definition of artificial intelligence. It may be difficult to define AI because of the interdisciplinary interest of researchers from linguistics, psychology, education, and neuroscience who relate AI to terms, ideas, and expertise in their respective fields. Higher education could be completely transformed by the continued development of AI-powered learning platforms like ChatGPT, Google Gemini, PictoBlox, teachable machines, and cognimate AI platforms. Writing tools, chatbots, natural language processing, images, computer codes, and other media and industry-related queries can all be produced by these AI technologies. Additionally, it has altered how we perceive the world and how education is delivered. In order to achieve significant changes in teaching abilities, content, talent training methods, and education governance, it is imperative that education and artificial intelligence be integrated in light of the world's constant change. The hubs of talent concentration and technological innovation are colleges and universities.
[0004] The most common applications of AI technology were in engineering courses, where they were typically used for prediction and profiling. Lastly, an analysis of AIEd in HE from 2007 to 2018 by Zawacki-Richter et al. identified four main applications of AIEd: intelligent tutoring systems, adaptive systems and personalization, assessment and evaluation, and profiling and prediction. The first obstacle is the requirement for significant funding for studies that combine intelligent machine learning methods with the concepts of human learning. This entails investigating AI's potential using various data streams in both theoretical and real-world settings. Furthermore, learning takes place in complex sociocultural contexts, underscoring the significance of comprehending how teachers can smoothly incorporate AI-enhanced approaches into their lesson plans. The development of education is inextricably linked to the construction of socialist modernization.
SUMMARY
[0001] In view of the foregoing, an embodiment herein provides a method for artificial intelligence in higher education: advancing teaching and learning through pedagogical intelligence. In some embodiments, wherein the PRISMA principles were employed. In this study, an a priori roadmap for conducting a rigorous systematic review was provided by the PRISMA extension, which stands for Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols. Continued research and interdisciplinary discussion are necessary to improve users' perceptions of AI's reliability, debunk myths, and encourage responsible AI use. We can use AI to promote customized educational journeys while maintaining ethical standards and optimizing advantages for all parties involved if we adopt a cooperative approach. The question of whether artificial intelligence in higher education is intelligent and information-based distinguishes it from traditional education. One of the most significant aspects of artificial intelligence's advancement in higher education is this as well. It can optimize the exchange of learning materials.
[0002] In some embodiments, wherein to find those databases and journals, a Boolean search is developed and applied. To decide which studies will be included in the final study, a set of publications found through those searches are compared to inclusion and exclusion criteria. After that, the final set of studies' pertinent data is taken out and coded to align with the research questions. This constructivist idea, which is compatible with intelligent tutoring systems and other AI tools and applications, entails adaptably reacting to students' preexisting mental models. AI tools can now be purposefully used by teachers to improve student engagement and encourage conceptual change.
[0003] In some embodiments, wherein both a manual and an electronic search were used in the data retrieval protocol. EBSCOhost's educational databases were included in the electronic search. The Wiley Online Library, JSTOR, Science Direct, and Web of Science were then searched electronically once more. A full text search was done in each of these databases. The Boolean search contained terms pertaining to AI, learning, and higher education that were in line with the research topic and questions. To help students better understand the course material, chatbots and virtual assistants like Google Gemini and ChatGPT can offer real-time feedback and Q&A sessions. Students who use these resources perform better, become more self-assured, and become more eager to learn. The paper examines the parallels and discrepancies in the evolution of artificial intelligence education domestically and internationally using comparative advantage, based on the positioning of the current business mode and learning from foreign experience.
[0004] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0001] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0002] FIG. 1 illustrates a method for artificial intelligence in higher education: advancing teaching and learning through pedagogical intelligence according to an embodiment herein; and
[0003] FIG. 2 illustrates a method for codes and axial codes for assessment and evaluation according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0001] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0002] FIG. 1 illustrates a method for artificial intelligence in higher education: advancing teaching and learning through pedagogical intelligence according to an embodiment herein. In some embodiments, a manual search was also carried out to look at additional AIEd-related journals that might not be in the databases. This is significant because AIEd is a relatively new field, and databases may not yet index journals that focus on it. Among them were Computers & Education: Artificial Intelligence, the International Journal of Artificial Intelligence in Education, and the International Journal of Learning Analytics and Artificial Intelligence in Education. Examining how AI tools can support the process of knowledge construction and paradigm shift in education is necessary for a thorough explanation. This is accomplished by examining the potential of particular AI tools and applications, such as adaptive learning systems, AI-driven analytics, and intelligent tutoring systems, to develop dynamic and interactive learning environments. Higher education must become increasingly integrated as a whole due to the advancement of modern science, technology, and production; this is reflected in the integration of courses. In order to train students to adjust to the demands of social development and possess the abilities to tackle complicated problems, it is necessary to make basic education and professional education, application research, and development research permeate and cross carry out.
[0003] In some embodiments, the countries, years, author affiliations, academic levels, and domains in each group were coded using a process known as a priori, which is based on pre-established criteria. The academic department of the study's first author was used to code the affiliations of the authors. Since they are the study's principal investigator, the first authors were selected in accordance with established research procedures. The a priori codes of Student, Instructor, Manager, or Others were also used to code the intended audience for the AI. These artificial intelligence (AI) systems have been trained, and a significant amount of data has been obtained through in-depth training with both publicly available and third-party licensing data. Following this stage of data collection and training, algorithms for reinforcement learning from human feedback (RLHF) are used to improve the output. As a result, LLMs can produce language that is similar to human expression and perform well on a variety of language processing tasks, including OpenAI's Generative Pretrained Transformer (GPT) and generative AI's Google Gemini and Bard. Through practice, educators apply the current theoretical understanding of teaching to create a sophisticated and stable system of teaching behavior. According to teaching theory, teachers can develop their primary teaching skills by improving their own performance. Then, through repeated consolidation exercises, they can develop advanced teaching skills at the automation level, forming stable teaching skills.
[0004] In some embodiments, the main question of this study, which looked at how AI was being used in HE, was answered through inductive coding. In order to match the application of AI to pre-existing frameworks, researchers frequently employed an a priori framework in their existing systematic reviews on AIEd in HE. For this study, a grounded coding methodology was chosen in order to extract trends on AIEd in HE from the data. This is significant because it makes it possible to directly understand how AI is being used, as opposed to assuming that researchers are using it and fitting the data to preconceived notions. A key component of artificial intelligence, natural language processing (NLP) focuses on how computers and human languages interact. This feature allows computers to comprehend, interpret, and generate human language expressions, providing useful applications in a variety of sectors, such as education, management, law, translation, healthcare, retail, architecture, and transportation [4, 34]. The use of LLMs has significantly advanced our knowledge of and ability to apply AI in these fields. There has been a significant expansion of the library's function space. The corresponding problems must be resolved concurrently. Readers may be able to make better use of their time to learn about networks and, to some extent, avoid becoming engrossed in the vibrant and alluring world of networks if reading time is monitored and restricted.
[0005] FIG. 2 illustrates a method for codes and axial codes for assessment and evaluation according to an embodiment herein. In some embodiments, a constant comparative method was employed in the grounded coding design. Through an iterative process, researchers found key text in articles about the use of AI. From these initial codes, axial codes were created by continuously comparing the uses of AI with other uses of AI, then uses of AI with codes, and finally codes with codes. When most of the data fit into one of the codes, the code was considered theoretically saturated. GPT-4, one of Open AI's many iterations, includes important improvements like improved security and data safety protocols, access to the most recent data and multilingual support, text generation from images, and drug discovery tools. Similarly, with their material compositions, generative AI's Bard and the recently unveiled Google Gemini offer sophisticated options for completing a variety of tasks, from image recognition to speech recognition. Learners will have an unparalleled educational experience thanks to intelligent teaching and education. The new normal of ubiquitous learning and lifelong learning everywhere will also be highlighted by online autonomous learning that seamlessly blends with real-world scenarios and free human-computer interaction.
[0006] In some embodiments, with education having the highest affiliated group, our data suggests that educators are now more interested in taking the lead in these research initiatives. This could be because of the pandemic, the need to assist faculty in other fields, or the necessity for educators to investigate technology for their own instruction during the lockdown. This might also be the result of educators' increasing familiarity with AI tools, which is fueled by societal attention. The ability of AI to evaluate student data and offer individualized learning experiences is one of the main benefits of implementing it in education. AI algorithms can determine each student's areas of strength and weakness by gathering and evaluating performance data, enabling teachers to modify their lessons to fit each student's unique learning requirements. Students can learn at their own pace and achieve better academic results with this individualized approach. The efficient multifaceted collaborative development of the government, businesses, and universities, which offers support for algorithm improvement, teaching mode update, educational resource aggregation, and other aspects, is crucial for the future integration of multidimensional collaborative artificial intelligence technology into education.
[0007] In some embodiments, the data showed that students were given a lot of weight when using AIEd in HE. This user focus differs from a recent systematic review on AIEd in K-12, which discovered that teachers were given priority in AIEd studies in K-12 settings. It might seem that HE uses AI to give students more attention than K-12 does. The high number of student studies in higher education, however, might be because HE researchers can more easily access the student body and study their own students. The key players in the development of intelligent learning environments are people as an organizational design, talent models, and communication agents. Since transparency and explainability policies direct the establishment of an objective and integrated approach to learning systems with the aid of AI tools, ethics is the final dimension and is crucial to ensuring any institute's willingness to adopt AI culture. Lower-quality literature frequently has lower academic quality, which leads to an overwhelming number of problems and makes it difficult to understand the main concerns of academic research. Deductive reasoning comes in third. Prediction results are frequently delayed in the development process of research objects, as is the case in the artificial intelligence industry, and it can be challenging to make reasonably accurate judgments based on traditional data based on previous research methods for some rapidly changing research objects.
, Claims:1. A method for artificial intelligence in higher education: advancing teaching and learning through pedagogical intelligence, wherein the method comprises;
using AI algorithms to analyze this data and create customized learning experiences tailored to each student's needs, pace, and preferences;
providing immediate and actionable feedback, supporting student growth and self-assessment;
implementing adaptive learning systems that monitor student interactions, assess learning needs, and adjust content difficulty dynamically;
using AI to analyze data on attendance, participation, and grades to predict potential risks to student success;
applying AI to analyze student performance data and identify content areas that need improvement; and
implementing ethical AI practices in higher education is critical to ensure student privacy, equity, and data security

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