You're passionate about Educational Technology. What cutting-edge skills should you prioritize mastering?

Mastering AI is no longer optional for EdTech specialists passionate about driving change. It's the engine powering the next generation of personalized learning, data-driven insights, and intelligent tutoring systems. Think of it this way: AI is like a Swiss Army knife for educators, offering a multitude of tools to enhance every facet of teaching and learning. For instance, AI can personalize learning at a high rate, adapting content and pacing to student needs. It can automate tasks, freeing educators to focus on what matters most - building relationships and fostering a love of learning. And it can analyze massive amounts of data to provide actionable insights, enabling educators to identify struggling students early on.

The ability to analyze data is essential, especially in making data-driven decisions related to educational technology. Analyzing how students, staff, and faculty engage with systems is pivotal to determining what learning strategies to deploy and what and how systems are used. It also helps determine a strategy for an organization's future needs.

Data is the compass that guides us in the EdTech world. It's not just about numbers; it's about knowing the story behind them and using that knowledge to create more effective learning experiences. Think of it like a detective investigating a case. They gather evidence, analyze clues, and connect the dots. Similarly, as EdTech specialists, we collect data on student behavior, performance, and engagement also. We analyze these patterns to identify areas of strength and areas where learners might be struggling. But data analysis isn't just about identifying problems; it's about finding solutions. By understanding the data, we can make informed decisions about curriculum design, instructional strategies, and personalized learning pathways.

Generative AI offers unique opportunities for qualitative data analysis because of the ease and speed with which it can work with language. I have had several use-cases of faculty analyzing end-of-course surveys using AI. What used to take hours of reading now takes a few seconds to convert, and a few minutes to "talk to". However, a background in qualitative research methods will help ensure accurate prompting and evaluation of the data to be used. AI can make the data analysis process easier, but knowing how to structure the data, teach the AI what it is and how it should be interpreted, and how to structure the response requires practice.

Basic coding skills have traditionally been an asset in educational technology and instructional design. Languages such as HTML, CSS, JavaScript, Java, Python, and others have been beneficial at times. More important than a particular language has been an understanding of how coding works and how computers operate. However, AI and low-code tools are making coding skills less necessary. Popular and free generative AI tools can both write and troubleshoot many different coding languages. In the near future, a high-level understanding of coding languages and how they are structured may be sufficient. In my experience, it has always been helpful to know what I am looking at "under the hood" of a particular educational technology.

I'm really into Virtual Reality (VR) and Augmented Reality (AR). These tools can make learning super fun and real-life like. I'd love to be part of building interactive simulations where students can practice real-world stuff in a safe, virtual space. Open and Quality Access to all