AI Agents Academy

Chairs: 

Prof George Siemens researches how human and artificial cognition intersect in knowledge processes. He is co-founder, Chief Scientist and Architect of Matter & Space – an organization building resources to respond to the systemic impact of AI on learning and wellness. He is the founding Director and Professor of the Center for Change and Complexity in Learning (C3L) at University of South Australia and developed the Masters of Science in Learning Analytics at University of Texas at Arlington. George Siemens is the founding President of the Society for Learning Analytics Research (http://www.solaresearch.org/). In 2008, he pioneered massive open online courses (sometimes referred to as MOOCs). He is President of the Global Research Alliance for AI in Learning and Education (GRAILE – www.graile.ai).

A/Prof Srecko Joksimovic is an established researcher in learning analytics, learning sciences, and AI in education. He is also Co-Director of the Centre for Change and Complexity in Learning (C3L) at the University of South Australia. His research explores how technology and AI can enhance learning, foster collaboration, and support human development through meaningful human-AI interaction.

A/Prof Vitomir Kovanovic  researches  Learning Analytics and Artificial Intelligence in Education, with a particular focus on developing approaches for measuring the development of complex skills and competencies. He targets students’ self-regulation of learning and understanding how trace data can be used to gain a deeper understanding of learning processes. He currently serves as a Co-editor-in-Chief of the Journal of Learning Analytics (JLA), a peer-reviewed, open-access journal that disseminates the highest quality research in the Learning Analytics field. 

Dr Aneesha Bakharia, an innovative and versatile professional, began her journey as an electronics engineer before pivoting to a career in educational software development. Her diverse experience spans across the higher education and vocational education sectors, where she has excelled in various technical, innovation project management and learning analytics roles. As an early experimenter with generative AI, Aneesha pioneered the creation of a graphic novel using AI and explored the world of chatbot prompt engineering, demonstrating her commitment to pushing the boundaries of technology.

Overview:

The influence of AI on education presents a complex and uneven landscape. While students are rapidly integrating AI into their learning processes, making it an almost ubiquitous tool for everything from research to drafting assignments and faculty members are increasingly leveraging AI for tasks like curriculum development, grading, and personalized feedback, the broader university system has demonstrated a notable slowness in effectively adopting and integrating AI at an institutional level. This disparity between individual user adoption and systemic transformation raises questions about how higher education will adapt to the rapid technological advancements AI offers. The challenges include, but are not limited to, developing strong ethical guidelines, ensuring equitable access to AI tools, re-evaluating traditional pedagogical approaches, and investing in necessary data and technology infrastructure for a widespread and impactful integration of AI across the entire university.

 

AI agents, defined as autonomous AI systems capable of perceiving their environment, making decisions, and taking actions to achieve specific goals, are poised to significantly reshape the educational landscape and serve as a strong starting point for universities to make system-level responses due to the advancement of AI. Agents are anticipated to impact all areas of education, including personalized learning, automated assessment, and intelligent tutoring. Through their ability to tailor content and pedagogical approaches to individual student needs, AI agents can create highly customized learning pathways that accommodate diverse learning approaches. Their capacity for real-time data analysis and feedback can offer immediate insights into student progress, enabling more effective and targeted interventions. However, the ethical implications surrounding student data privacy, algorithmic bias, and the potential for reduced human interaction remain critical considerations that necessitate careful navigation as these technologies become more integrated into educational practices. Agents have the potential to touch and shape all aspects of the university experience – research, teaching and learning, and administrative. 

 

This Academy will introduce researchers, faculty, and leaders to Agentic AI and its relationship to learning analytics. 

Objectives:

  • Explore foundational AI and agentic concepts: Participants will gain a comprehensive understanding of AI agents, current prominent architectures, features (memory, tool use), and their specific applications within educational contexts in teaching and learning, research, and administrative functions.
  • Develop practical skills: Participants will engage in hands-on activities to build and deploy basic AI agents, utilizing relevant tools and platforms. Prominent agentic tools (including Langchain, Crew, Gemini, and others will be featured)
  • Identify research opportunities: Participants will identify and formulate novel research questions related to AI agents in learning analytics.
  • Engage in collaborative exploration: Participants will collaboratively brainstorm and discuss potential future directions and challenges in the development and integration of agentic AI in education, and leveraging the capabilities of existing learning analytics approaches
  • Develop Agentic AI strategy: Participants will leave with the critical components of an institutional “AI Agent Stack,” including technology needs, process and approaches, onboarding guidelines for researchers and academics, and resourcing requirements to deploy AI agents institutionally

Agenda:

  • Define AI agents and they role they might play in education
  • Agent Architecture and technologies
  • Agent development environments (tools, frameworks)
  • Context engineering and management
  • Multi-agent learning systems
  • Personalization and adaptivity
  • Building composite agentic systems
  • Evaluate case studies of agent deployment and mechanisms for applying to educational settings. 
  • Participants will work in small groups to extend the basic agent or build a new one with a slightly more complex task.

Application process:

This academy is ideal for educators, researchers, and practitioners seeking to build their capacity to engage with AI, particularly agentic systems, in meaningful and practical ways. As AI agents begin to reshape educational practices across teaching, learning, research, and administration, this academy offers participants the conceptual grounding and hands-on experience needed to navigate this evolving landscape. Through exploring foundational concepts, experimenting with agent development tools, and discussing real-world applications, participants will gain the skills and insights required to critically and creatively integrate agentic AI into educational settings. Whether you are currently involved in AI and LA initiatives or just beginning to explore their potential, this academy will provide valuable resources and collaborative opportunities to support your ongoing work.

Proposal Format & Submission Process

We are inviting applications from individuals who are planning to build, or have already begun developing, AI agents within higher education contexts. This includes researchers, educators, technologists, and practitioners who are actively exploring or implementing agentic AI systems in teaching, learning, research, or administration.

 

To apply to the LAK and AI Agents (LAKAIA) Academy, please submit a one-page statement addressing the following points:

  • A summary of your academic or industry background, and your current workplace context. We welcome faculty, researchers, students, and institutional leaders.
  • Your current career stage and future goals related to AI and learning analytics.
  • Key challenges you anticipate encountering in the short to mid-term as you work with AI and agentic systems in your role.
  • Your personal goals and expectations for participating in this academy.

Applications must be submitted via EasyChair at: https://easychair.org/my/conference?conf=lak26

Society for Learning Analytics Research (SoLAR)