Future Directions on Generative AI

Will generative AI bridge the educational divide? A glimpse into the possibilities and future directions for research enthusiasts

Opening thoughts

According to an article by UNESCO, over 250 million children were out of school; roughly around 16% of children and youth were deprived of their right to get educated.

While this report alarmed the world, educational stakeholders were forced to deal with the bitter fact that a significant portion of youth is being wasted and misdirected. The causes of such proportional differences are very complex to deal with, and they pose a huge challenge to bridge this gap. While these challenges were complex to deal with, a new perspective on education emerged that made everyone rethink the entire pedagogical convention which is Generative AI.

Generative AI has obviously prompted every domain to find shortcuts without compromising the quality of results. Education being no exception to this, several academic stakeholders began exploring these things with a keen eye for enhanced learning strategy. However, the actuality is not everyone equally benefits from it, and it is crucial to address the lack of equity. Let’s examine the current educational indicators and what generative AI can offer in this domain.

Understanding the role of Generative AI in education

The primary function of Generative AI is to create new content from learned patterns in existing data; these models are trained to generate synthetic data that are valid and accurate in terms of quality. This enables a number of applications that replaces the need where manual input is required.

As a result, learning methods are re-invented with these revolutions. They allow us to create modified learning patterns that suits our unique needs. This sudden emergence of Artificial Intelligence may overwhelm us and ignite curiosity. Consequently, we started to feel the touch of AI in everything we see, read, and hear.

Now, education has also started to experience the benefits of Generative AI. From writing assignments with ChatGPT to having a completely personalised AI tutor, it is a truly remarkable revolution. But the question is, will everyone equally benefit from this?

Will generative AI reduce the equity difference in education, or will it further increase the educational divide? The only way to find out is dedicated research.

Factors that influence educational difference

Current educational indicators commonly highlight the disparities among various cultural and economic backgrounds, even within the region. These differences arise from varied supplies of resources, social characteristics and economic structures that negatively influence education.

Regions with a conservative cultural background tend to have a more backward practice towards educating women; they have an adverse impact on gender equality at the root level. In addition, schools in disadvantaged areas lack the proper infrastructure and management, causing a significant compromise on the quality of education. It is also evident that students without reliable devices or internet facilities face difficulty in engaging with online learning benefits.

Clearly, all these elements that contribute to learning inequality are diversely too complex for a single authority to eradicate. It is technically possible to make a reform that entirely changes the course of the educational sector, but it sounds easier said than done. We cannot determine solutions purely based on instincts without sufficient research or empirical observation.

The Potential of Generative AI in Bridging Gaps

Synthetic AI has obviously changed the way we learn and work. They offer improved efficiency in less time and reduced effort, which can surely transform how we evolve.

Certainly, in education, it has a lot of potential opportunities, like individualised learning systems, student learning analysis, generating accessible content, personal tutoring and much more. They embody countless number of applications that provide a better learning experience altogether.

In a report from The Wall Street Journal, classrooms in China use brainwave tracking devices and AI cameras to monitor students’ engagement. This is just a fragment of what is yet to come in future.

For language barriers, AI can provide language support and translation tools that enable a seamless learning experience. It educates beyond the conventional limitations indeed. They can be trained for any specific requirement while producing efficient results.

Therefore, these ML and AI models have the potential to foster inclusivity and accessibility to disadvantaged learners through modifications that can reduce the educational divide and cultivate educational equity.

Challenges and Limitations of Generative AI

Despite the progress, educational inequality remains sturdy, as if it cannot be eliminated by any means. The causes are beyond what they look like on the surface. Various factors with various backgrounds have resulted in this. To ascertain the solutions, we must analyse the challenges and limitations first.

Digital divide in education

Individuals from marginalised communities have been heavily impacted by the digital divide that makes them inaccessible to the privileges of AI. It will further increase the knowledge gap between them. Additionally, there is also a concern regarding the adequate operational knowledge to interpret AI output. Much like economic conditions, technology accessibility plays a crucial role in educational equity.

Quality of AI-generated content

AI is still in its infancy and has a long way to go for reliable usage that we can trust, if possible. Interpreting their outputs requires a strong familiarity with AI, as said earlier. XAI (explainable AI) is often known as a practical solution that makes them reliable, but they are still in development. Making AI’s content quality and reliable is a mandatory part of implementing them for practical usage.

Potential biases in AI systems

Biases can stem from irregular training data, which can create unwanted prejudice which may perpetuate social inequalities. To remove these biases, it is necessary to train on diverse datasets and implement inclusivity in their design systems.

These are just a few areas that have significant research gaps that promise to bridge the educational gap if researched properly.

Future Directions for Research and Development

Future Directions	Scopes
Ethical considerations and implications	Understanding ethical implications of Generative AI in teaching and learning practices.
Bias mitigation in AI	Developing AI systems free from biases to make them neutralised.
Adaptive learning systems	Adapting learning techniques that match the individual needs of the students from under-resourced areas.
Resource allocation and policy making	Making informed decisions to address disparities in education.

Why it is a concern

Before the arrival of AI, education was merely treated as a right. It was seen as a privilege among the majority. Those from a poor economic or resourceless background cannot think of having a quality educational experience. For marginalised learners, it was complex to tailor the learning strategies.

But soon after the emergence of AI, advancements in education started to rise, and it offered innovative solutions. Yet, some places need adequate exploration to make them accessible to all kinds of learners, such as a lack of equity in resources, which is a major obstacle that stops everyone from equally benefitting. Besides that, implementing inclusive measures in Generative AI is also a topic that needs to be explored. It is crucial to address these issues to bring equality in education and reduce disparity in education.

How to practically address them

For any such problem, it is essential to do a complete study and analysis to arrive at a solution. When it comes to utilising Generative AI to eliminate the differences in education, holistic research is essential. An appropriate methodology and literature review is necessary for an original contribution. As a single person, it will be extremely difficult to achieve research success. However, PhD Assistance has a professional suite of services to help you attain academic success. Providing a wealth of resources and skilled research specialists, you can critically explore to the deepest extent of your research.

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