Evolving Education with Small Language Models
Generative AI provides opportunities for education that can not be ignored.
When we launched in April, our press release lay emphasis on how education was wholly underprepared for the transformation that it was about to witness in the age of GenAI. “To ignore this would be negligent” read our release back then, and today, seven months on, we sit on the precipice of a paradigm shift in how students learn and educators teach thanks to Generative AI.
Yet, it is becoming increasingly clear that for many institutions, concerns over data privacy, content appropriateness, and alignment with educational standards are proving a barrier to widespread GenAI adoption. This is why we thought it good to detail a solution that navigates these concerns in a way that is safe, secure and effective. And — not to give away the keys to the kingdom so to speak — here is an overview of what such a solution looks like and enables.
A cornerstone this solution is a Small Language Model (SLM), a form of language model that has emerged as a promising avenue for institutions by offering a blend of efficiency, personalisation, and adaptability at a fraction of the computing cost of traditional LLMs.
But what are SLMs? And how will they transform education? This article delves into the role of SLMs in education, exploring their potential benefits and envisioning the future of learning with these advanced AI models via solutions like our own.
The Evolution of AI in Education and the Decline of the Victorian Method
The educational landscape has witnessed significant shifts over the centuries. One of the most enduring methods, rooted in the Victorian era, emphasised rote memorisation and strict discipline. Students were expected to remember vast amounts of information, often without understanding the underlying concepts or context.
Originating in the 19th century, rote learning prioritised memorisation over comprehension. Back then — and in many ways still today — the classroom environment was rigid, with students often reciting lessons and facing punitive measures for mistakes.
While this method produced students who could recall information verbatim, it often failed to foster critical thinking, creativity, or a genuine understanding of subjects. The emphasis was on 'what' rather than 'why' or ‘how’, and gave way to those who are “book smart” but not necessarily of high IQ or EQ.
In the age of AI, being “book smart” is becoming increasingly reductive. Large Language Models are trained on levels of literature unreadable in a single lifetime, and have coincided with a period of exponential technological change which has led to skills such as critical thinking and creativity taking pole position in terms of desirable skillsets (according to the WEF’s Future of Jobs 2023 Report).
As society has evolved and the demands of the have workforce changed, there is a growing recognition of the need for a more holistic, student-centric approach to education. That is not to say that being learned is redundant — knowing what to ask these tools is increasingly paramount after all — but it is to say that Small Language Models, in particular, offer a route away from rote learning. Instead of just memorising, students can now interact, question, and receive tailored feedback, fostering a deeper understanding of subjects and instilling burgeoning skills such as prompt engineering as as result.
What is a Small Language Model (SLM)?
As many know by now, language models are powerful tools capable of understanding and generating human-like text. At their core, language models are trained on vast amounts of text data, learning patterns, structures, and nuances of language. Their primary function is to predict the next word in a sequence.
While LLMs like Chat-GPT, have made headlines, they often require significant computational resources. SLMs, on the other hand, are more lightweight, making them ideal for real-time applications and integration into various platforms, including educational ones.
In this sense, LLMs and SLMs represent two ends of a spectrum. While LLMs are trained on vast amounts of diverse data, SLMs are more domain-specific, often tailored to specific industries or use cases. This specificity is achieved by training them on domain-specific data, often sourced from within the enterprise (in this case a school’s Learning Management System).
An SLM might begin its life with foundational training akin to an LLM but is subsequently fine-tuned to focus on domain-specific data. Unlike many traditional AI models that enterprises deploy, which focus on tasks like cluster analysis or linear regressions, language models are neural network-based models that specialise in studying tokenised text. This allows them to understand relationships between texts and generate coherent and relevant content.
One of the most notable benefits of training SLMs on domain-specific data is the reduced risk of models generating inaccurate or irrelevant content, often referred to as "hallucinations". Such inaccuracies typically emerge when models grapple with knowledge gaps, attempting to fill them based on limited information. Furthermore, the domain-specific nature ensures a walled garden which protects data quality, maintains privacy, and mitigates biases.
Personalised Learning
In traditional classrooms, educators often grapple with the challenge of catering to a diverse group of students, each with their unique learning pace and style. SLMs, with their ability to adapt in real-time, present a solution to this age-old problem.
Tailored Content: Imagine a classroom where each student receives content tailored to their understanding level. Advanced learners are challenged with complex problems, while those struggling are provided with foundational materials to bridge their knowledge gaps. SLMs make this possible, adapting to individual student needs and ensuring that learning is always at the right pace.
Learning Styles: Not every student absorbs information the same way. While some may prefer textual explanations, others might benefit from visual diagrams or interactive sessions. Recognising this diversity in learning styles, SLMs can present information in a format best suited to the student. For instance, in a history lesson about the Renaissance, while one student might receive a detailed textual explanation, another might get a visual timeline of key events, all generated on-the-fly by the SLM. Research has already shown that Generative AI bridges the gap between the below average and above average employees, this will be the same with regards to the classroom.
Bridging the Gap Between Query and Clarification
In the traditional educational setup, students often find themselves waiting for feedback, be it on assignments, projects, or simple queries. This waiting period can sometimes hinder the learning process. Small Language Models promise real-time feedback, revolutionising the student-teacher interaction dynamic.
Immediate Responses: Gone are the days when students would jot down their queries, waiting for the next class or a scheduled meeting to seek clarifications. With SLMs, they receive instant feedback. Whether it's a complex mathematical problem or a nuanced literary interpretation, the model provides clarifications, corrections, and suggestions, ensuring continuous learning.
Consistent Availability: The learning process isn't confined to school hours. While it may more uncommon than teachers like for students to burn the midnight oil or dedicate weekends to revisions, there are still those who do. SLMs can cater to these off-hours, ensuring students have a reliable source of age-appropriate information and assistance round the clock.
Maximising Output with Minimal Input
Educational institutions, especially those with limited resources, often face the challenge of providing quality education to a large number of students. SLMs offer a solution, ensuring resource efficiency without compromising on the quality of education.
Scalability: One of the standout features of SLMs is their ability to cater to a vast audience. Whether it's a small classroom of 30 students or a massive online course with thousands of participants, the model scales effortlessly, providing personalised content to each user.
Reduced Workload for Educators: Teachers, often stretched thin with administrative tasks, grading, and lesson planning, find a reliable ally in SLMs. With the model handling routine queries and assignments, educators can redirect their focus to more complex teaching tasks, fostering deeper student engagement and refining the curriculum.
Making Learning an Interactive Experience
The traditional lecture-based teaching method, while effective, often leaves little room for interaction. SLMs promise to transform this passive learning experience, making it more interactive and engaging.
Interactive Learning: With SLMs, lessons transform into dynamic sessions. Students actively engage, question, and explore topics, making learning a two-way street. For instance, a lesson on photosynthesis can turn into an interactive Q&A session, with the model generating diagrams, answering queries, and even posing challenges to the students.
Diverse Content Generation: The versatility of SLMs shines through in their ability to generate diverse content. Be it crafting short stories for a literature class, generating problem sets in mathematics, or simulating scientific experiments, the model ensures freshness and diversity in learning materials.
Beyond the Curriculum
While curricular knowledge is vital, the modern world demands a diverse skill set. SLMs, beyond their primary function, play a pivotal role in fostering these skills.
Prompt Engineering: Interacting with SLMs is not just about receiving information; it's also about asking the right questions. As students craft prompts for the model, they hone their skills in critical thinking and precise communication, learning the art of prompt engineering.
Self-directed Learning: The presence of a responsive tool encourages students to explore topics autonomously. This fosters a sense of independence and curiosity, traits essential for lifelong learning.
AI for Enhanced Teaching
While students stand to gain immensely from SLMs, educators too find value in these models, using them as tools to enhance their teaching methods.
Lesson Design: Crafting a lesson plan is both an art and a science. With SLMs, teachers can input core objectives and topics, receiving in return suggestions for lesson structures, activities, and supplementary materials. This AI-assisted lesson planning ensures comprehensive coverage of topics, making lessons more engaging.
Dynamic Adjustments: No two batches of students are the same. Recognising this, teachers, with the help of SLMs, can quickly adjust lessons based on real-time feedback. If a particular topic proves challenging, the model can generate alternative explanations or additional exercises, ensuring no student is left behind.
Collaborative Planning: The true potential of SLMs is realised when they work in tandem with educators. By combining the teacher's expertise with the model's vast knowledge base, lessons become more comprehensive, dynamic, and engaging.
The Transformative Horizon of Small Language Models in Education
The essence of learning is set to evolve, moving away from rote memorisation to a continuous journey of skill acquisition and personal growth. Ethical considerations will be paramount, ensuring that as we harness the power of GenAI, we uphold the values of privacy, fairness, and inclusivity. The role of SLMs will expand from mere tools to collaborative partners, co-creating content alongside educators and students. As we look ahead, the fusion of technology and pedagogy offers a vision of education that is inclusive, engaging, and empowering, heralding a future where every learner has the tools and opportunities to realise their potential in a safe and secure way.
If you would like to learn more about our SLM solution and seize the opportunities enabled by Generative AI, get in touch and we will help you adopt and adapt to the future of education.