our homework helper ai

our homework helper ai

The Role of Artificial Intelligence in Homework Assistance

1. Introduction to Homework Helper AI

Artificial intelligence (AI), enhanced through machine learning and natural language processing, is enabling a new generation of powerful and often extremely convincing tools capable of understanding and potentially fooling even the most skeptical individuals. Despite being challenging for AI to perform all of the tasks humans are capable, it is child’s play for modern algorithms to outperform humankind in a limited number of well-scoped operations. Homework assistance is one such example; here, AI could have educational potential by reducing the stress associated with homework for students, instructors, and parents. The Virtual Homework Helper (VHH) applications help, support, and monitor the student’s problem-solving process to achieve the learning aims of homework, assist teachers with monitoring homework completion and quality, as well as aid the teacher in creating high-quality assignments.

In this chapter, we describe the development of the VHH system as well as an experiment conducted to evaluate a particular version by determining its impact on the student’s stress level. The results of this experiment were found to be complex and cannot be reduced to a simple plus or minus for the use of the VHH. This paper is part of a series that investigates multiple aspects of using artificial intelligence in education. The complexity of this particular subject is apparent from conflicting education needs, including the role of homework in education, the educational value of completing homework, the importance of structure in learning environments, how to handle the very challenging educational need of low performers, pedagogical models for problem-solving, and how learning about problem-solving can occur, as well as conflicting technology needs, such as technology’s role in society, potential roles of artificial intelligence in education, how to prove the effectiveness of AI in educational research, what properties of AI to use in learning environments, as well as the support for novice users.

2. Benefits and Challenges of Using AI in Homework Assistance

Advances in artificial intelligence (AI) have enabled developers to engineer AI-based educational technologies that help students with their homework and reduce the time needed for students to carry out their assignments. This paper presents the recent artificial intelligence advances and studies the AI-powered educational technologies developed for the homework assistance tasks. Briefly speaking, in comparison with traditional educational support systems, AI can provide more customized, relevant assistance and be used at lower cost by having broader assistance coverage. Moreover, compared to using human experts, AI educational technologies have the advantages of less or no human errors and being operated 24/7.

When AI educational technologies are poorly designed or used, some challenges come along. AI is not suitable for all educational purposes, and there are some factors to consider before it is used. AI’s advantage has been challenged by the lack of emotions, unreliable data quality, and high development cost. Moreover, students may cheat if they over-rely on the AI educational support technologies during their homework, and some think the technologies can replace the traditional educational purposes and teachers. Therefore, how to optimize the solution and rule, as well as encourage students to use AI educational technologies correctly and efficiently, is an important topic that educators and developers can discuss and research further.

3. Ethical Considerations in AI-Powered Homework Help

Ethical usage of AI holds great significance as the growth of intelligent learning becomes ever more pervasive and its impact becomes dependent on our concerns and actions about how educational data are used in order to benefit our students. We summarize major ethical/aspirational documents that guide responsible AI through the lens of this special application. These guidelines are used to support sharing these experiences in an accessible manner. For each context, we take an approach built on the fundamental idea that for AI to make predictions that reliably serve an educational purpose, stakeholders must be able to trust the nature of these predictions and must have confidence in the ethical adequacy of algorithms used to generate them. Rather than recommending each appropriate standard, we propose a heuristic: for each context, construct information from AI prediction models in a way that reflects the rule.

Educational authorities across various global jurisdictions have published guidelines to help guide the design and use of the AI used for public educational purposes. In March 2019, the European Commission’s independent expert group on AI published “Ethics guidelines for trustworthy AI”. The fourth chapter of these guidelines, the part on the themes and content of AI, consists of seven core elements. While these guidelines certainly represent progress with guiding AI in general, the discussion of education-specific uses of AI in the community is less developed. As we build AI solutions for secondary and higher education, professional standards specific to the ethical use of AI during teaching and learning will be invaluable. The AERA has made strides in promoting the ethical use of AI in educational data, most recently focusing on fairness in automated decision-making systems.

4. Future Directions and Implications of AI in Education

The role of AI in homework preparation and assistance has a broad set of implications in education. In this work, we present an AI homework preparation and assistance system named AI2withC, which effectively uses AI to help students prepare for their exams and finish their homework. We analyze 13 million homework questions submitted by the students from different universities nationwide in China from 2005 to 2019. By employing modern NLP models like BERT and RoBERTa, we further explore the potential of future AI assistance for students in their curriculum study. In the future, AI will play a more and more critical role in many domains, including education (e.g., easily obtaining precise and timely feedback). However, Mayer determined a lack of empirical evidence from well-designed studies to support the effectiveness of intelligent tutoring systems based on current AI technologies. That set up seems to suggest the conclusion that AI has little to offer education.

Education must leverage gains in the ability of AI to help increase, deepen, and expand human learning. Intelligent support of meaningful student activities may help students learn more and progress more rapidly than possible in the traditional classroom. Once AI tutors can simulate the behavior of human tutors more effectively, such interaction with computers might come to influence interactions with humans. By focusing on improving intelligent tutoring at the intersection of AI development and educational research, such a simulationist approach may help establish a better educational research strategy. This work demonstrates that we can develop effective AI tutor systems and analyze the potential of AI tutoring. One consequence of a technology that can teach or help guide what students are doing beyond the reach of the human is that we should rethink as completely as possible exactly what we want our students to do.