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Executive Summary and Main Points
The recent trend in AI utilization has seen a shift from enterprises solely focusing on large language models (LLMs), such as GPT-4, to also embracing small language models (SLMs) and other non-LLM AI technologies. IT leaders recognize the benefits of SLMs, which offer greater specificity, more controlled data usage, and reduced occurrences of AI-generated errors known as “hallucinations”. Microsoft and Apple are exemplifying this trend, with the former rolling out Phi-3 SLMs in April, and the latter releasing eight SLMs for handheld devices. The development of bespoke AI tools like Homeowner AI by HomeZada demonstrates the potential for specialized applications of AI in the industry. Major considerations for selecting the size of AI needed for a project include factors like response time, cost, data privacy, and specialized needs.
Potential Impact in the Education Sector
The education sector stands to be significantly impacted by the recent adoption of SLMs, especially in Further Education and Higher Education where personalized learning experiences are paramount. These smaller AI models can cater to specialized subjects and niche academic fields, allowing for highly focused, efficient learning tools. For instance, SLMs can facilitate tailored feedback for specific courses or aid in language learning through custom-built conversation models. In the realm of Micro-credentials, where learners aim to skill up quickly and efficiently, SLMs can power responsive, competency-based learning platforms that adjust to individual progress in real-time. Strategic partnerships can evolve as educational institutions collaborate with technology providers to develop unique AI-powered learning and administration solutions, leveraging digitalization for competitive advantage.
Potential Applicability in the Education Sector
In global education systems, AI and digital tools can heighten learning experiences and administrative efficiency. SLMs could enable customizable tutoring systems that adapt to student learning styles, improve engagement through interactive learning modules, and assist in the grading process by evaluating short-answer responses with specialized models. Furthermore, AI could help international education institutions manage diverse datasets, including student records and research outputs, ensuring compliance with different countries’ data privacy laws. Innovative applications could also include AI-facilitated student support services, career advising, alumni engagement, and predictive analytics to reduce dropout rates and enhance student success.
Criticism and Potential Shortfalls
Despite their growing popularity, SLMs are not without critique. Potential shortfalls of smaller AI models in the education sector might include over-specialization leading to inflexibility or the failure to capture the breadth of knowledge required across interdisciplinary studies. Ethical considerations such as data privacy and consent, particularly when dealing with sensitive student information, must be addressed. Cultural implications are another concern, as AI applications in global higher education must be sensitive to the diversity of student populations. Comparative international case studies show that a one-size-fits-all approach fails to accommodate different educational and cultural contexts, and localized AI solutions may be necessary to truly meet the varying needs of international students.
Actionable Recommendations
For AI technologies to benefit the educational sector effectively, recommendations include initiating pilot projects focused on specific challenges within institutions, such as student retention or personalized learning. International education leadership should invest in training for educators and IT staff on the use and maintenance of these AI systems. Additionally, the establishment of ethical guidelines for AI deployment in education should be prioritized to protect students and ensure equitable outcomes. Strategic insights for international education leadership also suggest forming consortia for sharing AI resources and expertise, thus lowering the barrier to entry for smaller institutions. Lastly, engaging in continuous evaluation of AI tools applied in educational settings should guide iterative improvements, ensuring that technological advancements align with educational goals and values.
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Source article: https://www.cio.com/article/2139985/it-leaders-go-small-for-purpose-built-ai.html