Context: The Massive Open Online Courses have gained a lot of currency in the last one decade. However, use of Artificial Intelligence has provided impetus to the growth of MOOCs in the recent years.
Background of MOOCs
- Between 2008-2011, a new genre of online courses titled ‘Massive Open Online Courses’ (MOOCs) made their entry, driven by reputed institutions of learning.
- Stanford University in 2011 brought first institutional course using MOOC model.
- Most known and famous MOOC was edX launched by Harvard and MIT in joint effort.
- As of 2021, there existed nearly 35 MOOCS Learning Management Systems (LMS) spread across North America, Asia and Europe.
- India’s ‘Study Webs of Active-Learning for Young Aspiring Minds’ (SWAYAM) launched in 2017 by the Ministry of Education, Government of India. It is one of the world’s largest learning e-portals.
Challenges faced by MOOCs
- Fragile financial situation
- Very high operating cost and maintenance expenses
- Steep marketing cost
- Very low and competitive fee structure
- High dropout rates by entry level learners.
However, in the recent years, artificial intelligence has become a vital tool to make MOOC more interactive and attractive as a platform. In the current case Generative Artificial Intelligence has made an impressive performance in retaining the utility of the MOOCs.
What is Generative AI?
- Generative artificial intelligence refers to a subset of AI techniques and models that are designed to generate new content or data that is similar to, or indistinguishable from, real human-generated content. It involves training models to learn patterns and structures from existing data and then using that knowledge to generate new, original content.
- Generative AI models can be used in various applications such as image synthesis, text generation, music composition, and even video generation. These models are often based on deep learning techniques, particularly generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), or transformers.
- GANs, for example, consist of two components: a generator and a discriminator. The generator learns to generate new data samples, such as images, by trying to fool the discriminator into thinking that the generated samples are real. The discriminator, on the other hand, learns to distinguish between real and generated data. Through an iterative process of training and feedback, GANs can produce increasingly realistic and high-quality content.
- Generative AI has shown significant advancements in recent years and has been used in a wide range of applications, including art and design, content creation, data augmentation, and even in the development of realistic deepfake videos. However, it’s important to note that generative AI also raises ethical concerns, such as the potential for misuse, the creation of misleading content, and the need for responsible and ethical deployment.
Examples of Generative AI:
- OpenAI’s Generative Pre-trained Transformer (GPT)
- DeepArt or Deepfakes
How Generative AI could be game changer in MOOCs?
- It help students easily locate the suitable courses without any attempt to explore entire MOOC.
- AI can give challenges and task to students on daily basis without any repetition in language or pattern.
- AI coaching, counselling and virtual assistance. AI techniques like natural language processing (NLP) enable chatbots and virtual assistants to interact with students, answer their questions, and provide support.
- AI can assist in content creation and curation for online courses.
- AI can help in making learning more interesting, thereby retaining high enrolment ratios.
- AI can analyze student data, such as performance, learning styles, and preferences, to create personalized learning experiences. Adaptive learning platforms can use AI algorithms to tailor educational content and resources to each student’s individual needs.
- AI can help in breaking down the language barrier in real-time.
