our ai homework helper picture

our ai homework helper picture

Enhancing Learning with AI: The Role of AI Homework Helpers in Education

1. Introduction to AI in Education

Artificial intelligence (AI) has already become a standard part of everyday life for many in modern society. However, its use has been quite limited in primary and secondary education. To a great extent, education remains mired in dated teaching styles that rely on technology more suited to the 19th century than the 21st. While there have been some forward-thinking exceptions, the government education systems in most parts of the world remain heavily resistant to or frightened of change. To an extent, this is understandable. Teachers and administrators bear a heavy responsibility to educate the young that goes far beyond the teaching of subjects. A key part of the educator’s role is to provide mentoring and emotional and social counseling. Since children are the primary intellectual and social capital of any society, the burden of educational responsibility is enormous. Consequently, resistance to adopting a new approach is founded on good intentions and prudence. For this and other reasons, AI will play a less direct, more supportive role in education than its role in assisting us with our physical health.

At a basic level, the ways in which AI is playing significant roles in education fall into two categories. In the first category, AI has become adept at performing the tasks of grading, student management, and university course counseling. The activities of the second category directly serve the taking of lessons, such as chatbots for the purpose of teaching language, voice-powered language learning applications like “Amazon Echo” and “Google Home,” or instructive applications that use AI. One particular focus for the second category of application is for the AI to serve as a ‘homework helper’. This short review involves AI-designed intelligent tutoring systems (ITS) or question answering systems and introduces several such applications. However, few of these applications have undergone comprehensive, benchmark testing using relatively large-scale question sets. The authors have encountered some problems during the writing of this article. First, while many academic papers and research documents exist concerning AI-aided language learning and natural language processing, these rarely offer thorough experimental results and data analyses. Second, differences in definition and applications result in large numbers of papers proposing linguistic models and their applications in NLP, language processing, and Pidgin than actually conduct case experiments and evaluations. Third, it is hard to find research or a review that assembles and outlines essential information for readers or researchers. Therefore, this brief review inquires about these kinds of applications involving typical artificial intelligence and data applications. Its goal is to offer an overview of research in these areas of AI and language learning.

2. Benefits of AI Homework Helpers

In summary, the potential benefits of AI homework helpers are as follows. First, they can open up a new opportunity for improving classroom teaching by offloading repetitive homework support from teachers and thereby freeing up time for more creative, imaginative, and inspiring classroom activities that develop a range of teachers’ and learners’ skills. Second, the right kind of AI homework support, such as conceptual scaffolding, tailored feedback, or personal encouragement to try harder or to try different problems, might increase academic achievement and confidence in math and perhaps in other subjects. Third, this improvement in homework may positively feedback on attitudes to classroom teaching, increasing engagement, willingness to attempt difficult problems, effort, skill, and resilience during classroom-based learning as well. There remains, however, the question of whether any potential gains are worth the investment in AI homework helper development and use.

AI in education, or AIEd, including AI homework helpers, promises many benefits. In essence, AIEd can offer a powerful combination of fast, reproducible, individualized, and potentially effective and motivating, self-paced learning experiences with personalization in learning adaptable, appropriate, in-depth, time-efficient learning support for high-ratio classrooms that may compliment rather than replace, and thereby ameliorate some of the negative consequences of formal mathematics teaching from technology use both in and outside the classroom. This is potentially a great force for good. However, this must be balanced against the substantial challenges of realizing these promises, both in general and with respect to AI homework helpers. Moreover, particular attention must be paid to incentive structure to drive the development of AI tools of high value to society.

3. Challenges and Ethical Considerations

The use of AI homework helpers, while beneficial, also poses a real danger. Inappropriate and unethical use of AI can lead to the opposite effect – the burden of solving particular tasks is shifted from students to AI. As a result, the quality of solved problems remains at a low level, and often students miss out on the learning opportunities offered. There are ethical issues such as: ensuring that students understand solutions and improve learning. How should AI programs react if it becomes clear that the student doesn’t understand a particular solution? Should they offer more simplified guidance or suggest teachers and knowledge sharing apps that can help? Should the student know that the machine designed to learn actually learned from his mistakes?

Some of these ethical issues can be mitigated by properly controlling AI – adjusting the level of help, feedback, or even refusing to provide additional assistance and submitting the surrounding educational context. If the machine understands properly that its help is not appreciated, it can increase the learning process. AI can contribute to the educational process at the system level – by using it as a large contextual sensor that detects weak areas and suggests interventions. Nonetheless, delivering knowledge, using AI to verify that students have learned and provide educational experiences beyond the current capabilities of the educational system are just some of the low-hanging fruits in the global world of artificial intelligence.

4. Case Studies and Success Stories

In recent years, various spin-off projects and for-profit company efforts have been inspired by the general Homework Helper paradigm and specific course-going Homework Helper, where the problem set in question follows content in a fixed sequence of course material and is predictable before the course starts. During course time, the AI TA loop can be closed relatively quickly, with AI TAs tracking student errors very early in the course, and of course course instructors tirelessly producing more problem set problems each year. Previous work in this area has involved the development of several standalone Homework Helper systems, in courses with enrollment sizes ranging from 40 to 200 students. These projects grew incremental improvement on a series of major design decisions to produce Homework Helper systems accurate enough to be perceived to be useful, and the usage of the tools produced modest enough improvements in course learning outcomes that the materials are now integrated with the regular tool set used in their host courses.

We believe at least three course-going Homework Helper systems are now better than the average human TA on the homework grading and office hours staffs found today in the introductory physics course with 600 students and 50 humans. Only a system that uses AI-driven hint advice powered by natural language processing could answer a mixture of questions that typically occurs during actual student paying homework robot-graders are only fit to grade homework, virtual TAs are not complete for student learning support, intelligent tutoring systems are used almost exclusively with the simplest content and ignore the great unsolved problems and all as “simple AI solutions to real education problems.” More sophisticated systems are way worse than the average human TAs for the typical correct-every homework solution process needed by TAs. Being worse than human is not an Education Technology, putting faculty out of work production. Precisely because these systems can focus their data collection on problems that trip up a significant number of students, grade homework in minutes using data analytics, and provide differentiated feedback, we hypothesize that these systems can justifiably claim the Homework Helper is the course-going application of AI to schoolwork. We believe the role of AI-driven learning support to be at least as unapologetically progressive as other major ed-tech advances.

5. Future Directions and Recommendations

This paper provides an overview of some of the core research studies being carried out, focusing on AI homework help, with the ultimate aim of having AI at the service of education. It has become evident that students approach and use their learning during homework and project work very differently from doing exercises and tests in the classroom setting. For example, why homework? This implies that students desire to learn or master something that they do not already know.

Homework can be a challenge for many students in general, but is frequently particularly challenging for many students with special educational needs, or for students in low-income areas with overburdened teachers. Could AI systems and knowledge representation ontologies help in guiding and scaffolding the homework process for these students? This paper describes research to develop AI homework help, and three linked prototype systems that have been built as part of an ideal of bringing the AI to classrooms to aid students with their homework. A number of recommendations, based on lessons learned as well as pedagogical and technological insights, are presented. Potential limitations of the approaches taken are also discussed, along with plans for future work.