ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

Topics We Cover

Featured Research and Reports

We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

For Technical Leaders

CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

Back to Blog

North-West University: Exploring AI-Driven Conversations as Dynamic OER for Self-Directed Learners

Jeremy WeaverJune 9, 2025
Premium

The paper proposes that AI-powered conversations, like those from ChatGPT, can serve as dynamic and personalized open educational resources to support self-directed learning, while highlighting challenges such as ethical concerns and the need for proper teacher training and infrastructure.





Summary of Read Full Report (PDF)

This conceptual paper explores the potential of AI-driven conversations, such as those from ChatGPT, to function as dynamic Open Educational Resources (OER) that support self-directed learning (SDL).

Unlike traditional, static resources, AI-powered dialogues offer personalized, interactive, and adaptive experiences that align with learners' needs. The paper argues that these tools can nurture key SDL competencies while acknowledging ethical, pedagogical, and technical considerations.

Ultimately, the authors propose that thoughtfully designed AI-driven OER can empower learners and teachers and contribute to a more inclusive and responsive future for open education.

  • AI-driven conversations can act as dynamic OER to support SDL. AI-driven conversations, such as those facilitated by ChatGPT, have the potential to function as dynamic Open Educational Resources (OER). Unlike traditional static resources, these dialogues offer personalised, interactive, and adaptive experiences that align with learners' unique needs and goals. This dynamic capability contrasts with static OER.
  • AI supports core principles and competencies of Self-Directed Learning (SDL). AI-driven conversations and generative AI tools can nurture key SDL competencies such as goal setting, self-monitoring, and reflective practice. They support learner autonomy, responsibility, self-motivation, and empower students to take initiative, plan, and manage their learning processes. AI also enhances online collaboration, creativity, problem-solving, and communication skills, which align with SDL characteristics.
  • AI integration can enhance Open Educational Practices (OEP) and improve access and inclusivity.Integrating AI into OEP holds the potential to address long-standing challenges in open education, such as learner engagement, the wider reach and adaptability of resources, and inclusive access. AI supports the creation of diverse and inclusive learning resources, facilitating multilingual and culturally relevant content generation. This integration aligns with the values of access, equity, and transparency that underpin open education.
  • Significant challenges exist in integrating AI into open education. Key challenges include legal and ethical concerns related to copyright, data privacy, and potential biases in AI outputs. There are also technical limitationsdue to fragmented OER infrastructure and a critical need for teacher preparedness and AI literacy, as many educators lack the foundational knowledge and confidence to use AI technologies effectively.
  • Successful integration requires thoughtful planning, policy, and professional development. To effectively realise the potential of AI-driven OER for SDL within OEP, it requires thoughtful design, robust infrastructure, inclusive policies, and sustained professional development for teachers. Recommendations include developing ethical guidelines, investing in compatible OER infrastructure, promoting inclusive AI design, providing professional development focused on both AI literacy and SDL skills for teachers, and encouraging ongoing research.

Why does owning the AI stack matter?

ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing β€” so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.

  • You own all the code and the data

    Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform β€” the stack itself is yours.

  • Model-agnostic

    Run any LLM β€” Claude, GPT, Gemini, Llama, Command, or your own fine-tune β€” and switch providers without rewriting the platform.

  • No per-seat pricing

    Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.

  • Deploy anywhere

    Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY β€” a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

See the ibl.ai AI Operating System in Action

Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.

View Case Studies
Work with our team

Pilots, deployment, and full ownership

Most enterprise engagements are one-time, not subscriptions. You integrate ibl.ai with your own data, deploy it on your own infrastructure, and the engineering hours scale with the work β€” so the price tracks the scope, not your headcount.

Start here

Pilot

from $15K

fixed scope Β· fixed timeline

A time-boxed proof of value on your real data β€” not a slide deck.

Best for: Teams that want to see ibl.ai working before committing.

  • Deployed on your infrastructure or our cloud
  • 1–2 production agents wired to a slice of your data
  • One integration (LMS / SIS / SSO / data source)
  • Weekly working sessions with our engineers
  • Pilot fee credits toward a full engagement
Scope a pilot
Most common

Integration & Deployment

$25K – $80K

one-time Β· not a subscription

Full deployment integrated with your data and systems. Engineering hours scale with scope.

Best for: Organizations rolling ibl.ai out across a department, campus, or business unit.

  • Platform deployed in your VPC, on-prem, or air-gapped
  • Integrated with your data + identity (SSO / SAML)
  • Multiple custom agents built to your workflows
  • Engineering hours proportional to scope
  • You own the data Β· run any LLM you choose
Plan a deployment
Full ownership

Codebase Transfer + Custom AI Engineering

Six figures

perpetual license Β· you own the stack

We transfer the full source code. You own and self-host the entire platform β€” outright.

Best for: Government, defense, and enterprises that require perpetual ownership and sovereignty.

  • Complete source-code transfer + perpetual license
  • Dedicated AI engineering team on your roadmap
  • Custom agents, models, and integrations to spec
  • Air-gapped capable Β· zero vendor lock-in
  • Family-owned, New York–based long-term partner
Talk about ownership
You own the code and data Run any LLM β€” Claude, GPT, Gemini, Llama Family-owned & operated from New York, NY