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UX of NovoMind.ai App V1
How to Build An AI Agent Team in ChatGPT
Considerations when Building the template
Draft memory Approach
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Phase 0: Priming: Establishing the User’s Context and reasons to learn and formulating the Mentoring Plan

Introduction
Tool: This phase will use the AI UltraLearnflow app (Called the App from now on) and the ChatGPT Mentoring Planner.
Description:
Before beginning the LearnFlow process, the App will collect information about the learner, such as:
Context: Age, job experience in the area, and learning profile ( Qualifications and background knowledge).
Reasons to Learn: The driving force behind the Learner wanting to learn
Learner Goal: This will capture what the learner aims to achieve.

1.1. Tool: App

Once the users Context and Learning Goal are entered into the App, it will use this information to create a prompt for the ChatGPT Mentoring Planner to develop an Mentoring Plan tailored to the learner’s needs with a set of Learning Objectives.
The prompt will guide the ChatGPT AI Mentoring Planner Bot to create the users AI Mentoring plan, which will be used in Phase 2 to guide the selection of content and take the learner through applied examples that make mentoring effective and personalized.
The user will use the app to access the ChatGPT AI Mentoring Planner Bot.

1.2. Tool: ChatGPT AI Mentoring Plan Bot

The ChatGPT AI Mentoring Planner Bot’s instructions guide it to take the App prompt to formulate a plan with the Learner, using reflective thinking and an iterative process, that will Output a table with two columns:
Col 1 - Learning Objective - What the learner aims to achieve in the next session with the bot, and
Col 2 - Justification: Reason for the specific Learning Objective and how it is aligned to the Learning Goal.
Each row will contain a separate Learning Objective
Output file
The ChatGPT Mentoring Plan will generate the following an CSV file
Example:
|Learning Objective |Justification|

1.3. Tool: App

The Mentoring Plan CSV file will be Imported by the user to the App

Outputs Required for the Next Phases:
Learner context, including age and job experience.
Learning profile, including qualifications and background knowledge.
User Level of understanding
AI Mentoring Plan Table with defined learning objectives and reasons.
The user will paste the AI Mentoring Plan into the app, and each row will be used to identify videos that meet the learner’s needs to go through the SQ4R approach.

Phase 1. Scan and Curate:


AI-Assisted Overview

Objective: Use the AI Bot to efficiently identify content and generate an overview of possible Learning Material. Where a Learning already has Learning Material, this step may not be necessary.

2.1. Tool: App generates Phase 1 Prompt

Once the Learner has uploaded the Mentoring Plan, the Learner will do a final review of their Plan then press a button that takes the user to the preferred ChatGPT Scan and Curate Bot

2.2. Tool: ChatBot.

Approach:
The Bot will help identify:
How the video content aligns with the Learners learning objectives and Level of Understanding.
Main topics covered and timestamps for key segments.
For each highlighted timestamp segment , an unambiguous summary paragraph based on the presenter's transcript.
Learner Involvement: While the Bot provides the initial skim of potential content and ask the user which content they want to review if any.
Output:
Learners will be encouraged to review the output What is the Author Conveying, Summary of how sections possibly relate to the Learning Objective, List of Key Concepts.
The User will confirm which listed content they want a deep summary on
Workflow Functionality:
In Phase 2, the Bot will use a prompt generated by the app that instructs it to skim the selected Context and identify
What is the Author Conveying,
Summary of how sections possibly relate to the Learning Objective,
List of Key Concepts.
Innovative AI Uses:
Use AI to compare content across multiple sources and provide a summary of common themes, giving learners a broader context.
In Preparation Phase: The user will be given options on how the bot is to compare content across multiple videos, allowing them to tailor the depth and type of comparison to their needs. In many cases, the learner may know the exact task they want to achieve, so the focus of the search can be more direct.
Explanation: This will allow learners to see diverse perspectives, which enhances understanding and critical analysis.
After selecting the video(s), AI can generate a set of questions based on the skimmed video content to help learners a) confirm the video meets their learning objective and b) assess the learner's initial understanding.
Workflow: After AI skims the video and identifies relevant segments, have a Q&A session with the user to verify if the content is appropriate for the learner's needs.
Outputs Required for the Next Phase:
Content URL and details of Learning Objectives, topics, and key points.
Overview of video content with timestamp segments and summaries.

Phase 3. Question: Transforming Content into Draft Notes

Objective: Foster curiosity and create learning objective and RKQ that guide the learner through the Media content.

3.1. Tool: ChatBot.

Approach:
The Chatbot will assist in transforming the content into Draft Notes by asking the learner
how well well user understands about the Key Points in the content
to confirm RKQs that they want the content to address
These RKQ will help learners stay engaged by:
Stimulating curiosity.
Activating prior knowledge.
Highlighting important points for easy identification.
The Draft Notes will be generated that will detail the
Leaning Objective
RKQ, which learners will address in the app template in Phase 4.
Outline of each section in the Content detailing
Key Points,
Questions about each Section the user will be able to address in relation to the Learning Objective and RKQ
Learner Involvement:
The Chatbot will encourage Learners to: -
Verify the Key Points themselves and consider the Related Key Questions (RKQ). This reinforces understanding and encourages active engagement. Studies on active learning and metacognition suggest that verifying information independently enhances comprehension and retention (Chi, 2009; Brown, Roediger, & McDaniel, 2014).
Generate some RKQ on their own before relying on AI. This practice enhances critical thinking and helps in identifying what they find important or unclear. Research shows that self-generated questioning significantly enhances understanding and critical thinking (King, 1991; Rosenshine, Meister, & Chapman, 1996).
Assess whether the content is applicable to the Learning Objective and RKQ
Workflow Functionality:
In Phase 3, the Bot will:
Suggest RKQs for each bit of media and review and brainstorm to identify if they are appropriate. The learner can adapt these questions in the App in Phase 4.
Generate an overview, a detailed content outline highlighting the Key Sections of the context with a comprehensive and unambiguous list of bullet points for the main ideas.
Create new terms, and glossary definitions.
In Phase 4, this Markdown file will populate the SQ4R AI App template, allowing learners to further adapt the RKQ and start to work consuming the context and their revising their AI generated notes.
Outputs Required for the Next Phase:
A markdown file containing:
Content meta data (Name, URL, Description)
Learning Objective
RKQ
Outline by Section Name
Objective
Key Points
Comprehensive unambiguous set of bullet points for each Key Point
TimeStamp with URL for each Section
Possible
Question Title for.
Key Concepts
List of to dos aligned to the Learning Objective and accompanying Reason
Innovative AI Uses:
Depending on the learning objectives, the Bot can suggest different types of RKQ, such as factual, analytical, or reflective, to promote varied thinking.
The Bot will develop output with user using an iterative approach prompting the user with questions specifically aligned with the learner’s objectives or tailored to their learning style, enhancing personalization.

Phase 4. Revise AI Generated Notes

In this phase, users will upload the markdown file from the previous phase and actively engage with the video content, focusing on key points to enhance comprehension and retention. The SQ4R app will guide users through the following structured note-taking process:
Sections:
The Notes will be divided into segments, each representing a Section. The app will display one row for each Section.
Key Related Questions (KRQ):
Pre-generated by AI in Phase 3, these draft questions are designed to prompt critical thinking and deeper understanding of the content. They user will intailly
Structured Note Fields for Each Segment:
Highlights:
Summaries or key points extracted by AI, emphasizing the main ideas of the segment.
Relevance:
A field for users to assess and rate the importance or applicability of the segment to their learning objectives.
Notes:
An open text area for users to record personal observations, insights, or additional information gleaned during the video. Users are encouraged to pay attention to transitional phrases (e.g., "however," "therefore," "in conclusion") that signal shifts in ideas or conclusions. Recognizing these cues aids in understanding the logical flow and structure of the content.
Key Concepts:
A section for users to identify and list fundamental concepts or terms introduced in the segment.

4.1. Tool: SQ4R App.

Table:
Data relating to Learning Objective will be stored in the Table Learning Objective. This table will store data at the level of Time Stamp Section of the YouTube Video, and contain data from the Markdown file. The Time Stamp section,
The Table Learning material will save the Name of the Learning document and url and summary
Approach:
Learners will focus on relationships between ideas and look for indicators like:
More of the same: "also," "furthermore," "in addition."
Change in ideas: "but," "however," "yet."
Conclusions: "therefore," "in conclusion," "hence."
AI will define new terms and generate glossary definitions in learners' notes.
Assistance will be provided for understanding graphs and visual elements presented in the video.
Learner Involvement: Learners should actively watch the material and attempt to identify key points before referring to AI highlights. This helps build independent viewing skills and improves comprehension. Research indicates that active viewing fosters better comprehension and long-term retention, as it encourages learners to engage deeply with the material (Nist & Simpson, 2000; Ahrens, 2017).
Workflow Functionality:
In Phase 4, learners will use the AI-generated Markdown file to review content and customize their notes in the App.
Learners will actively consume the video, write questions or notes, and review the content independently.
Outputs Required for the Next Phase:
Structured notes for each timestamp segment.
Customized highlights, KRQ responses, and personal notes.
Innovative AI Uses:
AI can track the learner’s viewing pace and suggest optimal breaks or highlight sections that may require more attention.
Explanation: AI could identify sections where the learner seemed to struggle, recommending targeted breaks or providing additional resources.
The app could use AI to recommend supplementary videos or readings if it detects difficulty in understanding key concepts.
Workflow: This functionality would be integrated into Phase 6 when AI reviews the learner’s progress.

Phase 5. Recite: Active Recall in the App

Objective: Reinforce learning through active recall while viewing media.
Tool: This Phase will be done in the App.
Table: Data will be use
Approach:
Phase 0: Priming: Establishing the User’s Context and reasons to learn and formulating the Mentoring Plan
1.1. Tool: App
1.2. Tool: ChatGPT AI Mentoring Plan Bot
1.3. Tool: App
Phase 1. Scan and Curate:
AI-Assisted Overview
2.1. Tool: App generates Phase 1 Prompt
2.2. Tool: ChatBot.
Phase 3. Question: Transforming Content into Draft Notes
3.1. Tool: ChatBot.
Phase 4. Revise AI Generated Notes
4.1. Tool: SQ4R App.
Phase 5. Recite: Active Recall in the App
Phase 6. Review: Immediate AI Guidance
Phase 7. Revise: Ongoing AI Mentorship for Retention through Application of Learning Objectives
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