Transforming Education with the Power of Generative AI

February 27, 2024

AI-driven education is disrupting traditional teaching approaches and shaping the future. AI solutions for education analyze enormous data sets using smart algorithms, providing personalized and adaptable learning experiences. Students can now receive immediate feedback and access to immersive technologies like augmented and virtual reality in education. Conversational AI, like chatbots and virtual tutors, can offer quick assistance, promoting independent learning, answering questions in real time, and guiding students through the learning process. 

AI can improve student engagement with courses, interactive lectures, gamified classrooms, and more – which is why the AI education market is predicted to cross $20 billion by 2027. 

In this blog, let’s dive into the world of generative AI in education, uncovering how it can redefine the way our students learn. 

Traditional Teaching Methods vs. Generative AI in Education

In the familiar landscape of traditional teaching, the rule-based approach has been a cornerstone. Educators adhere to established teaching methodologies, delivering content uniformly to a class. This approach, while foundational, often struggles to accommodate the diverse learning needs and preferences present among students.

How does generative AI change this? Let’s compare traditional teaching with Gen AI-powered education.

Traditional Teaching Methods

Uniform Instruction

Traditional methods often involve a standardized approach to teaching, where the same content is delivered to the entire class. This one-size-fits-all model may not cater to individual learning preferences and paces.

Manual Content Creation

Educators rely heavily on manual content creation, spending significant time developing lessons, assessments, and supplementary materials. This can be time-consuming and may limit the range and diversity of resources.

Limited Personalization

The personalization of learning experiences is challenging in traditional settings. Tailoring lessons to individual student needs is labor-intensive, and educators may struggle to provide targeted feedback to every learner.

Fixed Resources

Educational resources, such as textbooks, are often fixed and may become outdated over time. Students may not have access to real-time information, limiting the scope for dynamic and up-to-date learning experiences.

Generative AI in Teaching

Adaptive Learning Paths

Generative AI excels in adaptive and personalized learning. It tailors lessons based on individual student preferences, abilities, and learning styles, providing a customized learning path for each learner.

AI-Assisted Content Creation

With generative AI, content creation becomes more efficient. The AI can assist educators in generating high-quality summaries, outlines, and even visual aids, freeing up time for educators to focus on refining teaching methods.

Customized Feedback

Generative AI enables the delivery of personalized feedback to students. It can generate tailored hints, suggestions, and feedback based on individual performance, fostering a more interactive and responsive learning environment.

Dynamic and Diverse Resources

Unlike fixed resources, generative AI allows for the creation of dynamic and diverse learning materials. It can generate up-to-date content, adapt to emerging trends, and offer a broader range of resources to cater to various learning styles.

Generative AI Models for EdTech Industry

There are various Large Language models that have been built to transform the education sector. These models are motivated by the research paper, Large Language Models in Education: Vision and Opportunities.

  • LinkBERT-large: LinkBERT improves training by leveraging document links, making it valuable in EdTech for understanding and generating content across multiple documents or topics. It enhances the ability to process and generate educational content that spans various documents or topics, facilitating comprehensive learning experiences.
  • CANINE-s:  CANINE-s operates character-level transformation through Unicode code points, valuable in EdTech for Language Learning, Text Analysis, Content Generation, and Accessibility enhancement. It facilitates language learning, text analysis tasks, content generation, and accessibility tools in education, contingent on fine-tuning quality and task specificity.
  • BigBird-RoBERTa-Large: BigBird, a sparse-attention based transformer, extends the capability of Transformer models to process and understand longer educational texts. It enables efficient processing and comprehension of extended educational content, addressing the challenge of handling longer sequences in educational materials.
  • ElasticBERT-Base: ElasticBERT, based on BERT, is fine-tunable for various language tasks, making it versatile for applications in EdTech such as text classification and sentiment analysis. It is adaptable to diverse language tasks in EdTech, enhancing capabilities in tasks like text classification, sentiment analysis, and more.
  • XLNet-Base-Cased: XLNet, utilizing generalized permutation language modeling, is suitable for tasks involving long context, such as reading comprehension. It supports tasks requiring consideration of long contextual information, enhancing capabilities in areas like reading comprehension within EdTech.
  • Albert-Base: Albert-Base is a variant of the Albert language model designed for efficient parameter sharing and enhanced training speed. In the educational context, its role could encompass various natural language processing (NLP) tasks. It can leverage efficient parameter sharing and accelerated training for improved performance in tasks like text analysis, language understanding, and content generation.
  • EduBERT: EduBERT is a BERT-based model specifically tailored for applications in education. It is fine-tuned on educational datasets to excel in tasks relevant to the learning domain. Tailored for educational contexts, EduBERT enhances the ability to perform tasks like text classification, sentiment analysis, and other language-related tasks, offering specialized capabilities for learning-focused applications.
  • Merlyn-education-corpus-qa-v2-GPTQ: Quantized version of Faradaylab's ARIA 70B V2, fine-tuned for the education domain, particularly suitable for answering questions based on provided context. It specializes in addressing question-answering tasks within the education domain, contributing to improved contextual understanding and information retrieval.
  • ARIA-70B-V2:  ARIA 70B V2, fine-tuned for the education domain, is versatile for tasks like text generation and question answering. It enhances capabilities in tasks such as text generation and question answering, contributing to the development of intelligent educational tools.
  • DistilEduBERT: Fine-tuned version of DistilBERT on educational data, suitable for learning analytics tasks. It contributes to learning analytics tasks in EdTech, providing insights and analysis based on educational data.

Applications of Generative AI in EdTech

  1. Adaptive and Personalized Learning

Generative AI is ushering in a new era of adaptive and personalized learning experiences, meticulously crafting content that aligns with individual preferences and aptitudes. Through the generation of bespoke questions, tailored feedback, and insightful hints, Generative AI can provide learners with a unique and adaptive journey. Moreover, it can go a step further by suggesting relevant resources, creating a dynamic environment that caters precisely to each learner’s distinctive needs.


Consider an online learning platform that uses Albert-Base to provide personalized feedback to students. Here’s how it might work:

  • Content Understanding: The student submits an essay on a given topic. Albert-Base reads and understands the content of the essay, including its main points, arguments, and structure.
  • Feedback Generation: Based on its understanding, Albert-Base generates personalized feedback. This could include comments on the essay’s strengths, areas for improvement, and suggestions for additional resources for study.
  • Adaptive Learning: Over time, as the student submits more essays, Albert-Base adapts its feedback based on the student’s progress. For example, if the student consistently struggles with using evidence to support their arguments, Albert-Base might provide more targeted feedback and resources on this aspect of essay writing.
  • Personalized Learning Path: Albert-Base could also recommend a personalized learning path for the student. For instance, if a student excels in creative writing but struggles with analytical essays, Albert-Base might suggest resources to improve analytical writing skills.

This is just one example. The possibilities for adaptive and personalized learning with large language models like Albert-Base are vast and continually evolving. They can be used in various educational contexts, from K-12 education to professional training and beyond. 

  1. AI-Assisted Authoring

In the realm of content creation, Generative AI’s proficiency in generating high-quality content efficiently can transform the course creation process. From producing succinct summaries and meticulous outlines to crafting captivating captions and even generating visual aids like images and diagrams, Generative AI can streamline the authorial journey. This will not only save time for educators but will also help them to focus on refining and delivering impactful lessons.


Consider a scenario where a writer is working on a science fiction novel for their creative writing program at a university. Here’s how LinkBERT-Large might assist:

  • Idea Generation: The writer is stuck and needs inspiration for the next plot point. They ask LinkBERT-Large for ideas based on the current storyline. The model generates several unique and creative suggestions, helping the writer overcome their writer’s block.
  • Writing Assistance: As the writer drafts their novel, they can use LinkBERT-Large to improve their writing. The model can suggest more engaging ways to phrase sentences, recommend more precise words, and help ensure the text is grammatically correct.
  • Consistency Checking: LinkBERT-Large can read the entire novel and check for consistency in the storyline, character development, and writing style. It can point out potential issues, such as a character who has changed too abruptly or a plot point that contradicts earlier events.
  • Audience Adaptation: The writer wants to adapt their novel for different audiences, such as translating it into another language or simplifying the language for younger readers. LinkBERT-Large can assist with these tasks, helping to ensure the adapted text retains the original meaning and tone.

This is just one example. The possibilities for AI-assisted authoring with large language models like LinkBERT-Large are vast. They can assist with various types of writing, from novels and screenplays to academic papers and business reports. 

AI smart content creation can also help with 2D-3D visualization, where students can perceive information differently. 

  1. Creative and Collaborative Learning

Generative AI can be a catalyst for nurturing creativity and fostering collaboration in EdTech. By providing a diverse array of challenges and stimuli, it can become the driving force behind learners’ exploration, experimentation, and co-creation. Through the generation of thought-provoking prompts, immersive scenarios, and illustrative examples, Generative AI can cultivate an environment where students actively engage with content and collaborate with peers. This dynamic approach to learning will stimulate critical thinking and creativity, setting the stage for a holistic and collaborative educational experience.


Consider a scenario where a group of students is working on a project to design a sustainable city. Here’s how XLNet-Base-Cased might assist:

  • Brainstorming: The students can use XLNet-Base-Cased to generate ideas for their city. They could ask the model questions like ‘What are some innovative ways to generate renewable energy?’ or ‘What are some strategies for reducing waste in a city?’ The model can provide a variety of creative and informed responses, sparking discussion among the students.
  • Collaborative Writing: The students need to write a report on their city design. They can use XLNet-Base-Cased to help draft and edit their report. The model can suggest more effective ways to phrase their ideas, ensure their writing is grammatically correct, and help maintain a consistent style throughout the report.
  • Presentation Preparation: The students need to prepare a presentation on their city design. They can use XLNet-Base-Cased to generate a script for their presentation, suggest compelling ways to present their ideas, and provide feedback on their delivery.
  • Peer Learning: As the students interact with XLNet-Base-Cased, they learn from the model’s responses. This can lead to a deeper understanding of the project topic, improved writing and presentation skills, and enhanced critical thinking as students evaluate and build upon the model’s suggestions.

This is just one example. The possibilities for creative and collaborative learning with large language models like XLNet-Base-Cased are vast. They can be used in various educational contexts, from project-based learning to peer tutoring and beyond.

  1.  Task Automation 

AI can help with a number of value-added tasks in schools and colleges. Along with creating a tailored teaching process, AI solutions can check the homework, grade the tests, organize research papers, maintain reports, make presentations and notes, and manage other administrative tasks. 

  1. Periodic Content Updates 

AI can allow students to create and update information frequently to keep the lessons up-to-date with time. Students also get notified whenever new information is added, which helps prepare them for upcoming tasks. 

  1. Multilingual and Other Support

Features like multilingual support can help translate information into various languages. AI can also play a vital role in teaching visually or hearing-impaired audiences. 

  1.  Assistance with Conversational AI 

Chatbots can help students and teachers with immediate responses to queries. Conversational AI can deliver intelligent tutoring by closely observing the content consumption pattern of a student and catering to their needs accordingly. 

People worldwide opt for distance learning and corporate training courses. Here AI chatbots can solve enrollment queries, deliver instant solutions, provide access to required study material, and more. 

  1.  AI in Examinations 

AI software systems can be used in examinations and interviews to help detect suspicious behavior and alert the supervisor. AI programs can keep track of each individual through web cameras, microphones, and web browsers and perform an analysis whenever any movement alerts the system. 

Benefits of Generative AI in EdTech

Enhancing Learning Outcomes

The transformative impact of Generative AI extends far beyond mere efficiency, actively contributing to better learning outcomes. Through the delivery of personalized and engaging content, it can catalyze the development of critical thinking, problem-solving, and creativity. By tailoring educational experiences to individual needs, Generative AI can play a pivotal role in shaping a learning environment that fosters excellence and achievement.


Consider a scenario where a student is studying for a Biology exam. Here’s how DistilEduBERT might assist:

  • Study Material Understanding: The student can ask DistilEduBERT to explain complex concepts in the study material. The model can provide clear, concise explanations in a way that’s easy for the student to understand.
  • Quiz Generation: DistilEduBERT can generate quiz questions based on the study material to help the student test their understanding. The model can also provide detailed explanations for the correct answers, helping the student learn from their mistakes.
  • Study Plan Creation: Based on the student’s performance on the quizzes, DistilEduBERT can create a personalized study plan. The plan would focus on the areas where the student needs the most improvement, helping them study more efficiently.
  • Progress Tracking: Over time, DistilEduBERT can track the student’s progress and adjust the study plan as needed. This ensures that the student is always focusing on the most relevant areas.
  • Exam Preparation: As the exam approaches, DistilEduBERT can generate practice exams that mimic the format and difficulty of the real exam. This will help the student feel more prepared and confident on exam day.

Reducing Costs and Workload

Generative AI can alleviate some of the burdens borne by educators and students. Its seamless automation of content creation tasks can not only save precious time but also mitigate costs. The optimization of resource utilization ensures that learners receive the most relevant, efficient, and effective content and feedback, thereby enhancing the overall educational experience.


Consider a scenario where an educational institution is developing an online course. Here’s how ElasticBERT-Base might assist:

  • Content Creation: The institution needs to create a large amount of educational content for the course. Instead of hiring a team of content writers, they can use ElasticBERT-Base to generate the content. The model can create detailed lesson plans, informative articles, engaging quizzes, and more, saving both time and money.
  • Content Personalization: ElasticBERT-Base can adapt the course content to meet the needs of individual students. For example, it can generate simpler explanations for students who are struggling with a topic, or more advanced material for students who are ready for a challenge. This ensures that each student receives the most effective and efficient learning experience.
  • Feedback and Grading: Grading assignments and providing feedback can be a time-consuming task for educators. ElasticBERT-Base can automate this process by evaluating students’ work and generating personalized feedback. This not only reduces the workload for educators, but also allows students to receive feedback more quickly.
  • Student Support: ElasticBERT-Base can serve as a virtual teaching assistant, answering students’ questions 24/7. This provides students with instant support whenever they need it, without the need for educators to be constantly available.
  • Course Improvement: Over time, ElasticBERT-Base can analyze students’ performance and feedback to identify areas where the course could be improved. This allows the institution to continuously enhance the course, ensuring it remains effective and relevant.

Increasing Access and Inclusion

A standout advantage of Generative AI in EdTech lies in its role as a catalyst for diversity and inclusion. By offering learners a rich tapestry of varied content and experiences, it dismantles traditional barriers, making education accessible to a broader audience. This inclusivity extends beyond geographical and linguistic boundaries, ensuring that learners worldwide can engage with educational content irrespective of their location or language proficiency.


Consider a scenario where an online learning platform is aiming to reach a global audience. Here’s how BigBird-RoBERTa-Large might assist:

  • Language Translation: BigBird-RoBERTa-Large can translate educational content into multiple languages, making it accessible to non-native English speakers. This ensures that learners worldwide can engage with the content, irrespective of their language proficiency.
  • Accessibility Features: The model can generate alternative text descriptions for images or diagrams, making the content more accessible to visually impaired students. It can also transcribe audio content for hearing-impaired students.
  • Cultural Adaptation: BigBird-RoBERTa-Large can adapt the content to be culturally sensitive and relevant to students from different backgrounds. This fosters an inclusive learning environment where all students feel valued and respected.
  • Personalized Learning: The model can adapt the learning material based on the learner’s proficiency level, learning style, and interests. This ensures that every learner, including those with learning disabilities, can have a personalized and effective learning experience.
  • Community Building: BigBird-RoBERTa-Large can facilitate online discussions among students from diverse backgrounds, fostering a sense of community and mutual understanding.

Impact of Generative AI Models in Indian EdTech Industry

The impact of AI models on the Indian EdTech industry is indeed profound and transformative, bringing about positive changes in various aspects of education. Here's a closer look at some key areas where AI is making a substantial difference:

  • Personalized Learning: AI-driven systems analyze extensive student data to understand individual learning preferences, strengths, and weaknesses. This enables the creation of personalized learning paths tailored to each student's unique needs. Adaptive learning platforms use AI to dynamically adjust the pace, content, and assessments, ensuring that students receive a customized educational experience.
  • Intelligent Tutoring Systems: AI-powered tutoring systems act as virtual mentors, offering real-time feedback and guidance to students. These systems can identify misconceptions in student responses and provide targeted explanations and additional resources. The ability of these systems to adapt to individual learning styles enhances the effectiveness of the learning process.
  • Automated Grading and Feedback: AI simplifies and expedites the grading process by automating the evaluation of exams, quizzes, essays, and coding assignments. Machine learning algorithms ensure consistent and unbiased grading while providing students with immediate feedback, fostering a quicker and more effective learning loop.
  • Predictive Analytics: AI-driven analytics tools utilize student data to predict academic success, identify students at risk of falling behind, and recommend targeted interventions. Early intervention techniques, individualized assistance, and personalized learning plans are facilitated by these predictive analytics models, enhancing overall student outcomes.
  • Enhanced Administrative Efficiency: AI streamlines administrative tasks in the EdTech sector, leading to increased operational efficiency. Automation of administrative processes such as enrollment, scheduling, and resource allocation allows educational institutions to focus more on providing quality education.

The projected growth and adoption of AI in the EdTech industry highlight its increasing importance. The statistics indicate that 47% of learning management tools will be AI-enabled by 2024 and the expected CAGR of 40.3% between 2019-2025 underscore the rapid integration of AI technologies into the education sector. This trend reflects the commitment of Indian EdTech companies to leverage AI for intelligent instruction design and digital platforms, ultimately enhancing the learning experience for students.

Challenges and Risks with Generative AI

Ensuring Quality and Accuracy

While Generative AI promises innovation, the challenge lies in mitigating the risk of inaccurate or inappropriate content. Vigilant validation, meticulous verification, and hands-on moderation by human experts become indispensable safeguards, ensuring the generated educational material maintains the highest standards of quality and accuracy.

Protecting Privacy and Security

The ethical use of Generative AI necessitates a steadfast commitment to stringent guidelines safeguarding privacy and security. Robust security measures are imperative to prevent potential misuse or unauthorized access to sensitive data, creating a secure and trustworthy learning environment for both educators and learners.

Maintaining Human Agency and Responsibility

In addressing concerns surrounding human agency, clear communication and comprehensive education take centerstage. Generative AI’s transparency in model outputs and decisions becomes pivotal in building trust and preventing dependency issues. By keeping users well-informed and empowered, Generative AI can be seamlessly integrated into the EdTech landscape while maintaining accountability and responsibility.

The Role of E2E Cloud in Developing Generative AI Models for the EdTech Industry

E2E Cloud plays a pivotal role in the development and deployment of AI models within the EdTech landscape. It offers notable contributions in several key areas which significantly contributes to the advancement of AI in the education sector:

  • AI-First Hyperscaler: E2E Cloud, recognized as a NSE-Listed AI-First Hyperscaler, boasts a robust and dependable infrastructure, providing users with a stable and secure environment for executing AI models.
  • Machine Learning Platform - Tir: E2E Cloud introduces Tir, its flagship machine learning platform, built upon the advanced Jupyter Notebook. This web-based interactive development environment offers cutting-edge features, including JupyterLab, enhancing user interaction with notebooks, code, and data.
  • Cost-Effectiveness: E2E Cloud stands out in the Indian and global markets by offering highly competitive pricing, presenting cost-effective solutions for the seamless execution of AI models.
  • Flexible and Scalable Infrastructure: E2E Cloud provides a resilient infrastructure tailored to support AI models, ensuring optimal performance and scalability for diverse computational requirements. Users have the flexibility to easily scale their resources based on specific needs.
  • Support for AI in EdTech: E2E Cloud actively facilitates the integration of AI in the EdTech industry by furnishing essential infrastructure and tools. This support contributes to the growth of EdTech by enhancing personalization and streamlining various educational tasks.

The Future Landscape

Looking forward, the future of generative AI in education is rife with possibilities. As technology continues to evolve, we can anticipate further refinements and innovations in the application of generative AI, contributing to an educational landscape that seamlessly integrates the advantages of advanced technologies. 

The potential for greater personalization, adaptive learning experiences, and collaborative educational environments holds the promise of revolutionizing the way we teach and learn. Embracing generative AI in education is not just a shift in methodology but a forward-looking venture into a future where the boundaries of traditional teaching are surpassed, opening doors to a new era of learning possibilities.

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  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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