Industry
Education
Digitizing education for blended and immersive learning experiences
HIGHLIGHTS
- As generative artificial intelligence (GenAI) continues to develop, it will have a greater impact on the education sector. In the future, AI-powered tutors, virtual laboratories, and personalized learning platforms will revolutionize learning.
- For a transformative initiative such as GenAI to be successful at an organizational level, the push must come from the top. This requires leaders to act as change agents throughout the transformation journey.
- With a balanced approach intertwining ethical governance with robust guardrails, carefully curated policies, and technological innovation, GenAI can be a game changer in enabling educational institutions with real, scalable benefits.
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GenAI as an enabler of growth
Organizations across industries that have adopted GenAI are witnessing measurable improvements in business-critical areas.
Current technological trends show that GenAI plays a pivotal role in accelerating growth. With rapid advancements in this technology and OpenAI’s launch of ChatGPT Edu–a specialized version of ChatGPT built for higher education–academic institutions must consider its impact across the education value chain.
To remain competitive and aligned with these advancements, education leaders must view GenAI as an enabler of productivity and operational efficiency at scale. Strategic adoption requires a proactive evaluation of how GenAI can seamlessly support teaching, learning, and administrative processes. Leaders must also establish guardrails to ensure responsible use while maximizing the technology’s benefits.
This paper addresses key considerations for educational institutions embarking on their implementation journey.
Implementing GenAI: Strategy and approach
GenAI goes beyond replicating existing information.
It analyzes patterns from training data to create novel outputs that align with the original characteristics while introducing innovation. GenAI’s versatility spans multiple media types, generating output that resembles human-created content such as photos, videos, music, text, software code, and ideas. Its ability to operate across modalities—such as converting pictures to narratives or vice versa—opens new dimensions for creative expression. ChatGPT, an artificial intelligence chatbot, is a form of text-based GenAI. Other popular examples include DALL-E 2, which can generate digital images from natural language descriptions.
This technology, however, is not new.
The concept of GenAI was introduced in the 1960s with early chatbots. However, its ability to create convincingly authentic images, videos, and audio of real people only became feasible with the advent of generative adversarial networks (GANs) in 2014. A type of machine learning algorithm, GANs analyze and learn patterns to create innovative content that reflects the original data’s essence.
Implementing GenAI at the organizational level is a transformative initiative. Leadership must champion this change from the top by fostering a participatory change mindset across the organization. Implementing GenAI in education requires strategic planning, budgeting, and risk management. Therefore, leadership intervention becomes critical considering that GenAI implementation is both a resource and cost-intensive activity.
Institutions should begin with pilot projects to assess business impacts, enabling informed decision making before moving on to full-scale development. A phased approach offers faster deployment at lower costs. Educational institutions are encouraged to start with low-complexity, high-value use cases before scaling up.
The following stages outline how GenAI can be leveraged, in increasing complexity:
- Consume as is: Use prompt engineering to interact with GenAI models and generate meaningful responses.
- Enhance productivity: Integrate GenAI into current IT ecosystems such as LMS, SIS, or CRMs to streamline processes and generate insights through automation.
- Contextualize LLMs:
- Adopt commercial GenAI models over custom enterprise data of educational institutions.
- Fine tune LLM models along with custom datasets to embed domain-specific knowledge, ensuring relevance for education-specific use cases.
- Build custom LLMs: Develop bespoke custom LLM models to fulfil niche education requirements.
The exponential improvement in GenAI’s outputs across text, code, images, and video is expected to outpace human capabilities through 2030. By strategically implementing GenAI, educational institutions can unlock new opportunities to enhance learning, streamline operations, and improve research efficiency.
The figure below depicts how educational institutions can map relevant use cases with requirements, possible applications, complexity, and changes in current state of GenAI.
Figure 1:
GenAI fitment evaluation in current IT state
Figure 1:
Top ten cases of GenAI in education
There are several applications of GenAI in education.
Key use cases have been elaborated below.
- Personalized adaptive learning experience
GenAI enables the creation of intelligent learning platforms that provide personalized and adaptive learning experiences. These platforms use machine learning algorithms to analyze student data, such as their past performance, skills, and feedback.
GenAI-based systems can leverage such information to identify knowledge gaps and provide recommendations and guidance. They can generate exercises, quizzes, and practice questions based on individual learning needs. Equipped with AI-powered resources, smart teachers can assist in real-time, monitor progress, and adjust teaching strategies to optimize learning.
- Content creation and curriculum design
GenAI can assist in creating course materials, including syllabi, lesson plans and teaching materials, such as questions for quizzes, exercises or explanations, and summaries of concepts. Teachers benefit from automated generation of content, saving time and improving resource variety.
GenAI also enables rapid development of e-learning capsules, micro-videos, and interactive multimedia elements tailored to course needs. Language learning platforms can correct grammar and create exercises and questions for language courses.
- Virtual lab, research, and experiments
GenAI, when integrated with virtual reality, can create simulations and virtual environments where students can conduct experiments, observe outcomes, and test predictions in real time. Such immersive environments make complex learning more accessible.
- Automated assessment and grading
GenAI-based tools such as ChatGPT and the Intelligent Essay Assessor can reliably grade essays and other written work and provide feedback on their performance. Such automated tools ensure speed, consistency, and objectivity. Various studies including by Mizumoto and Eguchi (2023) found that ChatGPT reduced grading time and delivered accurate and consistent results.
- Smart college search tools
GenAI can help students find suitable colleges by matching profiles with course offerings, costs, and locations. Training GenAI on the latest eligibility criteria, tuition data, and career opportunities allows students to make well-informed decisions about their higher education paths. A student looking for STEM programs with scholarship options, for example, can receive personalized recommendations, reducing search time and effort.
- Automated marketing content creation
Universities invest heavily in digital content for websites, microsites, and social media. GenAI tools such as Microsoft’s Dynamics 365 Copilot, can reduce effort in content editing and management by automating write-ups for websites and promotional materials.
- Restoring old learning materials
GenAI can restore and enhance outdated or low-quality learning materials such as historical documents, photographs, and films, by improving resolution and quality. These upgrades make materials more engaging and accessible across digital and print channels, enriching students’ comprehension.
- Virtual tutors and on-demand learning
GenAI enables personalized virtual tutors, providing students with real-time feedback and 24/7 academic support. This can be especially helpful for students without access to in-person tutoring. Universities have already started customized, self-paced programs that accommodate individual schedules.
TutorAI, for example, offers an interactive platform that generates interactive content on a variety of topics, allowing students to study anytime, anywhere.
- Multilingual learning and translation tools
Research shows that education in the native language is crucial for inclusion and quality learning. GenAI supports multilingual learning by translating educational content, including books, articles, and online resources, into multiple languages, making educational materials accessible to broader audiences. GenAI can also generate speech and create multilingual content, allowing students to learn and practice multiple languages, encouraging inclusion and cultural exchange.
- Chatbots and customer service
GenAI-based chatbots streamline student services by offering 24/7 support for academic inquiries, homework help, and administrative processes. These chatbots free up educators to focus on core tasks while providing seamless assistance during admissions, enrolment, and beyond.
Setting guardrails for effective deployment
GenAI offers transformative potential for education.
However, it also presents potential challenges that institutions must address to ensure responsible and effective use.
- Managing biases in educational materials
LLMs powering GenAI may inherit social biases from their training data, raising concerns about bias in content related to inclusion, race, ability, and religion. Human oversight remains essential to identify and correct these biases.
Therefore, institutions should conduct regular audits, implement bias-mitigation techniques, and train models on diverse datasets to minimize biased outcomes.
- Addressing inaccuracies and hallucinations
GenAI can produce inaccurate or outdated information since LLMs lack real-time updates. On average, most generative models are truthful only 25% of the time, according to the TruthfulQA benchmark test.
Training custom models is costly (the cost of training OpenAI’s GPT-3 was more than $5 million), requiring extensive data, hardware, and energy resources. However, advances like Massachusetts Institute of Technology’s claims to reduce the cost of training a LLM by 50% can lower these barriers.
Combining AI output with expert review helps ensure accuracy, while strategic investments in contextual data can mitigate misinformation risks.
- Navigating copyright ambiguities
Questions around ownership and copyright of AI-generated content remain unresolved. Are AI-generated works eligible for copyright protection?
Who holds the ownership rights over the content created? Can copyrighted content be used to train models?
Institutions must develop clear policies on intellectual property and ensure compliance with evolving legal frameworks.
- Preventing misuse and encouraging ethical use
GenAI can be exploited to produce misleading, harmful, or biased content. In the educational context, GenAI could be misused by generating false or misleading information that is presented as fact. This could lead to students being misinformed or misled. Moreover, students can misuse GenAI tools like ChatGPT for assignments, raising concerns about academic integrity.
Educators should integrate GenAI thoughtfully, using it to foster critical thinking through discussions, debates, and hands-on projects rather than as a replacement for learning efforts.
- Bridging the digital and linguistic divide
Limited access to digital infrastructure and the absence of local languages in LLMs deepen educational inequalities, marginalization, and exclusion. Closing these gaps is crucial for ensuring inclusive learning experience.
Institutions should expand access to digital tools and develop multilingual content to ensure equitable participation for all students.
Institutions must build the following guardrails to ensure ethical and controlled usage of GenAI.
- Content verification and bias mitigation
- Regularly review AI-generated materials for accuracy and bias.
- Use human oversight to validate content and fine-tune models to correct biases.
- Balanced AI integration and teacher training
- Treat GenAI as a complementary tool, not a replacement, for traditional teaching.
- Train educators on effective GenAI usage and promote a human-centric approach to learning.
- Privacy, security, and ethical frameworks
- Implement robust privacy policies aligned with legal standards like The European Union General Data Protection Regulation (GDPR) and Family Education Rights and Privacy Act (FERPA).
- Strengthen cybersecurity protocols to prevent breaches and ensure data integrity.
- Develop ethical guidelines emphasizing fairness, transparency, and accountability.
- Stakeholder involvement and feedback mechanisms
- Involve students, teachers, and administrators in identifying GenAI use cases.
- Conduct pilot programs and incorporate stakeholder feedback for continuous improvement.
Balancing skepticism and safety
GenAI can face heavy resistance from stakeholders.
The adoption of GenAI in education requires a careful balance between technological innovation and ethical governance. Institutions must build robust guardrails and continuously assess their AI strategies to ensure GenAI enhances learning outcomes. With well-structured policies and governance, GenAI can unlock scalable benefits in personalized learning, research, and administrative operations—ultimately transforming education for the better.
A strategic, top-down approach to GenAI implementation will enable institutions to maximize its potential while safeguarding against risks, ensuring that AI serves as a tool for progress rather than disruption.
