Unraveling Educational Needs: A Deep Dive into AI and VR Solutions in Higher Education

Introduction to Needs Analysis

As the realm of education undergoes transformative changes, the integration of Artificial Intelligence (AI) and Virtual Reality (VR) into pedagogical practices has captured the attention of educators and researchers alike. The Needs Analysis phase is a crucial step in harnessing the power of these technologies; it centers on identifying educational requirements that can be addressed through innovative digital tools.

Many applications marketed under the banner of AIEd (Artificial Intelligence in Education) aim to meet specific institutional needs, leading to a surge in interest surrounding their pedagogical applications. To explore these dynamics, we conducted two extensive datasets that delve into the perspectives of educators regarding the utilization of these emerging technologies.

Dataset 1: Insights from University Educators

In our first dataset, we engaged with 31 university teachers from diverse disciplines across 9 faculties, taking part in a series of 30 meetings over a period of 17 months (2022-2023). The participants varied in teaching experience, with nine holding professorial positions, and they articulated a range of insights concerning pedagogical innovations.

Within these discussions, we encouraged educators to share their visions on how AI and VR could enhance their teaching practices. The meetings, organized under the Serendip development project, created a collaborative environment where teachers felt supported in exploring new digital strategies.

Demographics of Participants

• Gender Distribution: 18 female educators and 13 male educators.
• Age Range: Participants varied from their early 30s to 60s, predominantly focusing on individuals in their 40s and 50s.
• Disciplines Represented: Across various faculties, the diverse academic backgrounds contributed to a rich tapestry of perspectives on AI and VR in education.

Dataset 2: K-12 to Higher Education Perspectives

The second dataset comprised insights gathered from 35 teachers across K-12 to higher education through a public competition hosted by the Technology Industries of Finland. This initiative aimed to incentivize educators to think positively about ChatGPT’s application in their classrooms, amidst a growing trend of banning such technologies from educational settings.

Participants shared their innovative ideas via Facebook groups, creating a platform for community engagement and collaborative brainstorming. With no demographic information required, the focus remained on the richness of the educators’ suggestions concerning the use of AI tools.

Methodology of Analysis

To systematically evaluate the wealth of qualitative data collected, we employed inductive thematic analysis. This approach allows for the emergence of themes directly from the data, fostering a deeper understanding of educators’ needs without pre-existing biases or assumptions.

Steps of Thematic Analysis

1. Familiarization with the Data: Immersive reading allowed researchers to identify patterns and meanings inherent in the discussions.

2. Initial Coding Generation: Preliminary codes were developed for Dataset 1 (28 codes) and Dataset 2 (22 codes), which encapsulated the central themes discussed by educators.

3. Theme Identification: Coding supported the grouping of similar findings into broader themes:

   • Dataset 1 Themes: Challenges in current teaching, Opportunities of emerging technologies.
   • Dataset 2 Themes: AI literacy fostering, AI as a character, Enhancing learning, AI as a teaching aid.

4. Review and Refinement of Themes: Critical reflection on initial themes ensured they accurately reflected the gathered data.

5. Defining and Naming Themes: Clear categorizations were made, reflecting on how themes relate to each other and informing the understanding of the research question.

6. Reporting Findings: Final insights were synthesized, illuminating how the identified needs align with the current educational landscape.

Implications for Pedagogical Design

After establishing the foundational needs of educators, we turned our attention to Pedagogical Design, a phase structured to address the educational needs identified in the analysis process.

Selecting a Framework

For sustainability education, adopting the sustainability competency framework provided a robust structure to define learning objectives and instructional methodologies. This framework aligned well with advancements in AI and VR, allowing for seamless integration into teaching strategies.

Steps for AI Character Design in IVR

1. Choosing the Framework: A clear reference structure was crucial for guiding decisions throughout the design process.

2. Defining Learning Objectives: Collaborative discussions with stakeholders ensured that learning goals were relevant and achievable within the immersive VR environment.

3. Designing Generative AI Characters: Engaging domain experts validated designs, focusing on their pedagogical utility and addressing the established needs based on feedback from earlier datasets.

Testing and Feedback from Domain Experts

Study 2 aimed to assess the interaction quality between educators and the generative AI (GAI) characters developed for pedagogical purposes. Through semi-structured interviews with 8 domain experts, we collected insights that would refine and enhance the characters’ functionality.

These experts engaged directly with the AI characters, mirroring real-world interactions in educational scenarios, and their feedback was instrumental in shaping the final iterations of the GAI characters.

Interviews and Response Validation

The interviews revolved around key questions designed to elicit detailed reflections on the GAI characters’ relevance and potential impact on pedagogical practices. This approach allowed researchers to identify necessary adjustments and ensured that the GAI characters truly reflected the pedagogical needs identified in earlier studies.

Conclusion

The Needs Analysis highlights an essential journey through understanding the educational landscape concerning emerging technologies like AI and VR. By engaging educators in meaningful conversations about their pedagogical aspirations, we can cultivate an educational environment that embraces innovation. The journey into thematic analysis helps clarify these aspirations while providing a roadmap for future pedagogical design aimed at enhancing teaching and learning experiences. As we continue this exploration, encouraging dialogue among educators about their needs will remain pivotal in shaping effective educational technologies.