This week our group continued to explore the intersection of technology and education, conducting further research, interviews, and prototype building. The central focus of our efforts was to address the pain points faced by students in classrooms, both physical and virtual, with a keen emphasis on digital accessibility. Our research honed in on the development of an AI-powered assistive learning prototype, specifically targeting the challenges associated with concentration and information retrieval in dynamic classroom settings.
Interviews: Unveiling Insights
Armed with a clearer perspective on the challenges we pinpointed in the last few weeks, we proceeded to conduct a series of interviews. The diverse set of interviewees included senior college students, middle school teachers, and coding instructors. Their insights provided invaluable glimpses into the nuanced issues surrounding concentration, distractions, and information retention during classes.
Test 1 — Senior College Student:
The first interviewee illuminated the challenge of pace in lectures and the subsequent impact on concentration. This insight informed our understanding of the need for adaptability in our prototype to cater to varying lecture speeds.
Test 2 — Middle School Teacher:
The middle school teacher’s observations of daily student distractions underscored the need for a tool that could assist both students and teachers in maintaining focus and summarizing key points.
Test 3 — Grad School Student:
Insights from the grad school student highlighted the importance of personalization, as different individuals cope with distractions and information retention in diverse ways.
Test 4 — Graduate Student:
The fourth interview emphasized the practicality of the prototype, suggesting the integration of engaging elements to enhance concentration and attentiveness during classes.
Test 5 — Graduate Student:
The fifth interviewee accentuated the need for efficiency in the prototype, proposing a detector-like tool that gauges students’ anxiety levels to alert teachers about potential concentration issues.
Test 6 — Graduate Student:
Insights from the female graduate student reinforced the significance of real-time feedback and efficient note-taking features in the prototype.
Test 7 — Coding Teacher:
The coding teacher’s preference for the recording AI highlighted the potential of such tools in aiding teachers in summarizing students’ strengths and weaknesses.
Buds, Roses, and Thorns Synthesis
In synthesizing our interviews and research findings, we adopted the “Buds, Roses, and Thorns” approach to identify potential opportunities, successes, and challenges.
Buds (Opportunities):
- Recognition of diverse student needs and preferences.
- The success of tangible outcomes from AI tools, providing real-time feedback and enhancing engagement.
- Opportunities to tailor AI tools to individual learning styles and preferences.
Roses (Successes):
- Positive responses to tangible outcomes from the first scenario, such as transcriptions and recordings.
- Acknowledgment of the benefits of engaging elements to enhance concentration.
- Successes in self-regulation skills and the efficiency of note-taking and summarization features.
Thorns (Challenges and Future Pain Points):
- Privacy concerns associated with monitoring and recording in AI tools.
- Potential information overload and challenges in balancing comprehensive information with relevance.
- The challenge of maintaining engagement in larger classes and addressing diverse student needs.
In the thorns lie not only challenges but also untapped opportunities for innovation. Privacy concerns associated with monitoring and recording can be transformed into an opportunity to pioneer robust privacy measures, ensuring students feel secure while using the tool. The potential information overload becomes a call for an intelligent algorithm that efficiently filters and categorizes content, transforming a challenge into a design opportunity. As we navigate the landscape of challenges and opportunities, the synthesis process becomes a compass, guiding us in building a prototype that is not just functional but transformative. Challenges become stepping stones to innovative solutions, and successes serve as inspiration for meaningful features.
Prototyping
As we delved into the synthesis of our interviews and analyzed the responses, the decision to prototype “Auto-generated Timestamps and Summaries” emerged as a natural choice. The recurring theme across interviews was the time-consuming nature of sifting through lengthy class recordings and the mental fatigue associated with distilling essential information. This pain point resonated across diverse perspectives, from students struggling with pace to teachers observing distractions.
The proposed prototype addresses this pain point by automating the process of identifying key moments in recorded classes. It aligns with the opportunities identified, providing tangible outcomes, personalized learning experiences, and real-time feedback. The positive responses to the efficiency of note-taking and summarization features further validated the choice of our prototype. In a landscape where time is a precious commodity, the idea of automating the extraction of vital information from recordings speaks directly to the needs articulated by our interviewees.
Armed with insights and a clear understanding of opportunities and challenges, our next step was to construct the information infrastructure/flow for our prototype:
1- recording class function: video, transcription, times stamps
2- real time AI notes
3- “traditional” text transcription
4- AI chat box: keywords auto-detection
The culmination of our research and planning led to the exciting phase of building prototypes. The prototypes were designed to showcase the potential functionalities of our AI-powered assistive learning tool.
- Auto-generated Timestamps: The prototype demonstrated the automatic generation of timestamps for key points in recorded classes.
- Summarization (AI notes): An efficient summarization feature that condensed lengthy discussions into concise, easily digestible content.
- AI Auto-Detected Keywords: A feature that automatically identifies and highlights key terms in the content, promoting interactive learning.
- AI Chat Box: A personalized guide, enabling students to delve deeper into highlighted key concepts through interactive conversations, providing comprehensive explanations, and supporting multimodal learning for an enriched and accessible educational experience.
Our week-long research journey was marked by a deep exploration of the challenges faced in educational settings and the potential solutions that AI-powered assistive learning tools could offer. The interviews provided crucial insights, and the synthesis process helped us identify key opportunities, successes, and challenges. Constructing the information infrastructure and building prototypes allowed us to materialize our vision for a tool that could revolutionize the learning experience.
As we navigate the future of education with AI, the focus remains on addressing challenges, refining prototypes, and ensuring that the technological advancements we introduce align seamlessly with the needs and preferences of students and educators. The path ahead is exciting, promising, and full of opportunities to create a more accessible and engaging educational landscape.
