User:AiDeveloper12/Artificial intelligence in education

Artificial intelligence in education

Artificial intelligence in education (AIEd) is the involvement of technology such as machine learning and advanced intelligent agents to create a learning environment which can be customized on a need basis. AI in education has transformed the traditional way of learning by usage of generative AI chatbots, self-assessing tools to analyze and a structured way of learning online through tutoring applications. Artificial intelligence provides quick access to knowledge and guidance which is one of the primary reasons for its adoption in the industry and the potential solutions that it could provide. While it is considered to be the future way of learning, it does come with concerns and challenges over ethics, bad practices and misinformation. This article will provide a reflection of how it started, the changing landscape of education through AI adaptation and the ethical concerns and challenges the education industry is currently facing.

Historical background

AIEd can be traced back as early as in the 1960s, when educators and researchers found the developing possibilities of computers in helping to learn. Computer-based instruction systems made use of program instructions for students to experience interactive learning outcomes. One such example is PLATO (computer system) which was developed by University of Illinois for the students. In the years 1970s and 1980s, the development of intelligent tutoring systems (ITS) were being adapted in classroom teachings. ITS provided instructions and materials based on performance, which provided a customized approach to learning. In the modern era, post-2000s natural language processing gave rise to AI usage, as students looking for information had interactions based on natural language. In recent times, Generative AI has been widely used for learning purposes. Applications such as ChatGPT, Perplexity AI have revolutionized worldwide.

Applications

Applications in AIEd can be a wide range of tools that can be used by teacher as well as students for learning outcomes. From primary classrooms to training facilities AI has evolved the way of learning through innovative and engaging delivery techniques.

AI based tutoring system

Intelligent tutors or Intelligent tutoring systems (ITS) such as SCHOLAR system in the 1970s was use for reciprocal questions being asked between teacher and students. ITS integrated four models the student model which was information about the student's abilities, the teacher model where based on analysis of student's performance strategies and guidance was provided, the domain model (knowledge of students and teacher), the diagnosis model where evaluation was made base on domain model. Although, it improved proficiency in studies, some studies provide negative results and claims of inefficiency than human tutoring were made.

Custom learning platforms

Personalized AI platforms are tailor made for individuals based on their strengths and weakness. The platforms make use of algorithms to predict students patterns and habits based on that they make recommendations to make improvement in their performances. Platforms such as LinkedIn, Duolingo are currently some of the popular companies providing the service. However, there is fair share of criticism as as these system based learning might provide isolation and student-teacher interaction may fade. Also, biasness in the train information might lead to misinformation.

Automated grading system

Automation assessment in grading students helps in saving time for the educator, providing immediate feedback. Systems make use of different rubrics combinations to grade performances. These systems need oversight as there might be scoring biasing.

Generative AI

AI tools such as ChatGPT, Grok (chatbot) fall under the category of generative AI, they provide results based on interactions and are very good in making use of search algorithms to give precised results to the user. However, there are risk involving over-reliance and violating academic integrity.

Ethical Challenges

With the advancement and adoption of AI, there are ethical challenges involved and proactive measure need to addressed to ensure equity and fairness to educators and establishments.[1]

Accessibility

Equal access to AI could be one of the areas that comes into consideration. As there may many low incomes and rural areas deprived of the platform use. This might widen the gap in terms of education access. Global efforts should be made to accessibility and train educators in those underprivileged areas.

Bias and fairness

AI agents might be trained on biased data according to different company driven agendas. This might lead to knowledge which is fed to them in form of misinformation. There should be policies and check to maintain such bias practices.

Data privacy

Data privacy is a ethical concern as most of the results are on trained data and it can be misused for various purposes. Compliance laws should make sure of the transparency and data privacy is intact.

Perspectives

Educator Perspectives

Educators and school administrations have found AI to be improving the efficiency of work done by a big margin, while some percentage of work force are concerned abut overreliance. Professional development is key to integrating AI effectively to ensue current jobs are not replaced.

Student Perspectives

Students are flexible, with technology such as personalized feedback and self-paced learning, but reliability, privacy, and fairness are concerns.

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Challenges and criticisms

Challenges involved are mostly about over reliance on the technology could lead to lesser creativity, critical thinking and problem solving abilities especially if students skip traditional methods. Algorithm errors, hallucination are some of the common flaws found today in AI agents, which sometimes makes it unreliable and less trustworthy.

References