Foram varas conversas, talvez espassadas… e algunmas conexoes pode ter se pedido my ADHD, Not have “fixed” things.. defined… but want to Gain clarity …

Novelty of the paper

About the company

Who product management is performed?
1. Have the role?
2. Specific training, competences developed?
3. Processes?
4. How product success is measured? (if ultimately product management is about maximizing product value)
What are the current business challenges?
Analysis - How or which areas of PM could help with these challenges?
Assuming you believe of IT, choices:

Do not do .. Prefer … Want to to do Compliment Complete replace you..

Key feedbacks and thoughts

JAN - I am SUPER interested in seeing how AI can complement/improve/replace any function in a company, including product management.
Helena - The question is how you can include that outside perspective in your research https://gemini.google.com/app/ee808a37da656df1
Narrowing down:
- What is specific
- What is the GAP?
  - Assess the potential impact of AI on a startup’s software product management practices.
    - Few
AI and Software Product Management (CORE)
Few selected articles:
AI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET” (Ogundipe et al., 2024)
Artificial Intelligence in Product Management” (Mahajan, 2024)**
“Artificial Intelligence, Scientific Discovery, and Product Innovation (Rodges, 2024)
AI Product Management 2 Years In” (Cagan, 2024)

Research Focus

Option 1 (Focus on assessment):

“Develop a framework to assess the potential impact of AI on a startup’s software product management practices, specifically during the phase of optimizing and scaling these practices.”

Option 2 (Focus on specific areas):

“Identify key areas within a startup’s software product management lifecycle (e.g., roadmap planning, user feedback analysis, prioritization) where AI tools can be implemented to enhance efficiency and effectiveness during a period of growth and improvement.”

Option 3 (Focus on the “why” of improvement):

“As a startup refines its software product management practices, explore how AI can address current challenges and unlock new opportunities in areas such as [mention specific areas like user engagement, market analysis, etc.].”

These options provide a more general perspective by:

Focusing on “impact” rather than specific actions (replace/improve): This allows for a broader consideration of how AI can influence product management.
Highlighting the stage of the startup: It emphasizes that the application of AI is being considered within the context of a growing company actively improving its processes.
Using “software product management practices” instead of “function”: This is more specific and relevant to the context.

Iteration 4

Hi Vitor,

Thank you for sharing the text. Well done!

I read this as more of an overall description of the research you want to pursue rather than an outline for an SLR (since you mention a literature review, several case studies and a series of papers). Is this correct?

A few thoughts and questions from my end:

While I agree that start-ups operate in a context that might make the adoption of PM practices difficult it is not fully clear why this is and what the problem with this is. What is it that you see as the main problem and what are the consequences of this problem?
The fact that AI “can be distracting” and that it is applied in ways that don’t necessarily increase value sounds as a general problem that could be true also outside start-ups. Or? I think you would benefit from trying to pinpoint what is specific for start-ups and avoid those things that might apply to other/all types of companies.
What exactly is the gap you are referring to and that you seek to solve?
In RQ1 you focus on GenAI. Is this the focus you aim for (there are other AI technologies)?
In RQ2 you focus on the design of a framework and how a framework can be developed. Is it the design process itself you seek to study?

The literature and the references make sense to me. What I would be interested in is a search string to learn about the specifics you intend to look for in your SLR.

Also, the literature review cannot include everything you seek to achieve. You can provide an overview of existing literature and existing frameworks as the basis for proposing something new (and to summarize what is missing). However, keep it somewhat narrow as the development of the framework will be a later step and most probably based also on case studies (if I understand you correctly).

R2D2 Proposal

Here’s a revised version of your research proposal draft incorporating the feedback you received, focusing on clarifying the problem, its consequences, and the specific gap for startups:

1. Introduction

The rapid advancement of artificial intelligence (AI), particularly generative AI, is transforming how products are conceived and managed. These AI-powered tools have the potential to revolutionize product management, impacting every stage of the product life cycle. Already, AI is proving invaluable in areas like development, marketing, and customer support, facilitating rapid prototyping and enhancing product adaptability [REF].

Startups, however, operate in a context of limited resources, agility, and constant pressure to demonstrate rapid growth, which can hinder the effective adoption of established product management practices [REF]. This isn’t just about adopting practices, but about optimizing them for the unique startup environment. Many startups rely on lean methodologies and iterative development, often prioritizing speed over structured processes. While this agility is crucial for survival, the lack of tailored product management practices, especially in the context of rapidly evolving AI capabilities, can lead to several key problems:

Misaligned AI Implementation: Startups may adopt AI tools without a clear understanding of how they contribute to overall product strategy and value creation. This can result in investing in AI features that don’t address core customer needs or differentiate the product effectively, wasting precious resources.
Suboptimal Resource Allocation: The hype around AI can distract startups from focusing on fundamental product development principles. They might chase the latest AI trends without a strategic roadmap, leading to scattered efforts and inefficient resource allocation. This is particularly critical in resource-constrained startups.
Difficulty Scaling: As startups grow, the initial “learn-as-you-go” approach to product management, often exacerbated by haphazard AI adoption, becomes unsustainable. Without a structured approach to integrating AI into their product management practices, they struggle to scale effectively, leading to inconsistent product development and missed market opportunities.
Lack of Competitive Advantage: While larger companies may have the resources to experiment with AI across various functions, startups need to be laser-focused. A strategic approach to AI-driven product management can be a significant differentiator, allowing startups to outmaneuver larger competitors by rapidly identifying and addressing niche market needs.

The fact that AI “can be distracting” and that it is applied in ways that don’t necessarily increase value is a general problem, but it’s magnified in startups due to their limited resources and pressure to achieve rapid growth. They have less room for error and less time to recover from missteps. The evolving nature of AI adds another layer of complexity, as startups must not only adopt product management best practices but also integrate AI strategically and adapt their practices as AI capabilities evolve.

The gap this research project aims to address is the lack of a structured framework specifically designed for startups to assess and strategically leverage the potential of AI within their software product management practices. While resources exist on general software product management maturity [REF], they don’t address the unique challenges and opportunities presented by AI in the startup context. This research will develop an initial framework to help startup founders and leaders leverage AI, improve product performance, outperform competitors, and stay focused on areas where AI can be most relevant to their business. As part of a series of papers, this can be extended to other contexts, like different organization sizes, etc.

2. Research Questions

How can generative AI be specifically leveraged by startups to optimize and scale their software product management practices?
How can an artifact like a framework be designed to guide this implementation effectively?

3. Literature Review

The literature review should cover:

AI and Software Product Management (CORE) Few selected articles:
- AI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET” (Ogundipe et al., 2024)
- Artificial Intelligence in Product Management” (Mahajan, 2024)
- “Artificial Intelligence, Scientific Discovery, and Product Innovation (Rodges, 2024)
- AI Product Management 2 Years In” (Cagan, 2024)
Software Product Management and Startups
- Provide background around application of SPM and startups, specifically focusing on the challenges they face in adopting and adapting practices due to resource constraints, rapid growth pressures, and the need for agility.
- Include literature on lean methodologies and iterative development in startups and how these interact with the adoption of structured product management practices.
- Explore existing frameworks for software product management maturity and their limitations in the context of AI and startups.
- Probably a few others related to decision making, framework design, and the intersection of AI and agile development.

By focusing on the specific challenges startups face and the need for a tailored framework, you’ll strengthen your research proposal and make a more compelling case for its importance. Remember to cite relevant literature to support your claims.

Iteration 3

Submitted

1. Introduction

Startups, however, operate in a context of limited resources, agility, and experimentation, which can hinder the adoption of established product management practices [REF]. AI presents both opportunities and challenges for these startups. On one hand, AI tools can create greater efficiency, enabling startups to evolve their practices. On the other hand, the evolving nature of AI can be distracting. Startups may not apply AI gains to activities that increase product value, or they may see individual improvements without realizing overall effectiveness.

Evaluating maturity and strategically choosing where to invest are crucial steps in evolving some practice. While numerous resources exist on evaluating software product management maturity [REF], the evolving role of AI adds a layer of complexity. This makes it difficult for startups to define and evaluate their practices effectively, especially as they scale. Though generative AI can help startups mature their practices more rapidly by automating tasks, providing insights, and enhancing efficiency, effective implementation still requires careful selection, prioritization, and organizational readiness.

This research project aims to address this gap by developing a framework to assess the potential impact of AI on a startup’s software product management practices. Through a comprehensive literature review and case studies, this project will create an initial framework to help startup founders and leaders leverage AI, improve product performance, outperform competitors, and stay focused on areas where AI can be most relevant to their business. As part of a series of papers, this can be extended to other contexts, like different organization sizes, and etc.

2. Research Questions

How can generative AI be specifically leveraged by startups to optimize and scale their software product management practices?
How can an artifact like a framework be designed to guide this implementation effectively?

3. Literature Review
The literature review should cover:

- AI and Software Product Management (CORE)
  Few selected articles:
  - AI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET” (Ogundipe et al., 2024)
  - Artificial Intelligence in Product Management” (Mahajan, 2024)**
  - “Artificial Intelligence, Scientific Discovery, and Product Innovation (Rodges, 2024)
  - AI Product Management 2 Years In” (Cagan, 2024)
- Software Product Management and Startups
— Provide background around application of SPM and startups
- Probably a few others related to decision making, framework.

Complete text

1. Introduction

2. Research Questions

How can generative AI be specifically leveraged by startups to optimize and scale their software product management practices?
How can a artifact like a framework be designed to guide this implementation effectively?

3. Literature Review

The literature review will cover a range of topics related to AI in SPM, including:

AI and Software Product Management
Software Product Management and Startups
Introducing SPM for a Startup
Case studies of successful and unsuccessful Gen-AI implementations

4. Methodology

This research project will employ a mixed-methods approach, combining qualitative and quantitative research methods. The following methods will be used:

Literature review: A comprehensive review of academic articles, reports, and other relevant literature to understand the current state of knowledge on AI in education.
Empirical research: Surveys, interviews, and observations to gather data on the experiences and perceptions of students, teachers, and administrators regarding AI in education.
Case studies: In-depth analysis of specific educational institutions or programs that have implemented AI to understand the factors that contribute to successful or unsuccessful outcomes.

5. Timeline

The research project is expected to be completed within a timeframe of [Number] months. The following is a tentative timeline:

Month 1-2: Literature review and development of research instruments
Month 3-4: Data collection (surveys, interviews, observations)
Month 5-6: Data analysis and interpretation
Month 7-8: Report writing and dissemination of findings

6. Deliverables

The following deliverables are expected from this research project:

A comprehensive research report detailing the findings of the study
A presentation of the research findings at a relevant academic conference
A publication of the research findings in a peer-reviewed academic journal

7. Budget

The estimated budget for this research project is [Amount]. The budget will cover expenses related to:

Literature acquisition
Research materials and equipment
Travel expenses for data collection and conference attendance
Publication fees

8. Ethical Considerations

This research project will adhere to the highest ethical standards. All participants will be fully informed about the nature of the research and their rights. Informed consent will be obtained from all participants before any data collection takes place. Data will be anonymized and stored securely to protect the privacy of participants.

9. Conclusion

This research project is expected to make a significant contribution to the understanding of the ethical implications of AI in education. The findings of this study will be of interest to educators, policymakers, researchers, and other stakeholders involved in the development and implementation of AI in education. By addressing the ethical considerations and challenges associated with AI, this project aims to promote the responsible and beneficial use of AI to enhance teaching and learning experiences for all.

Pieces - Maturity

Evaluating maturity and strategically investing in training are crucial steps in implementing any new practice. While numerous resources exist on evaluating product management maturity, the evolving role of AI adds a layer of complexity. This makes it difficult for startups to define and evaluate their practices effectively, especially as they scale.

Pieces - Others

This project addresses this gap by developing a framework to assess the potential impact of AI on a startup’s software product management practices. Through a comprehensive literature review and case studies, this framework will guide startup founders and leaders in leveraging AI to improve product performance, outperform competitors, and stay focused.

The rapid advancement of artificial intelligence (AI) is transforming various aspects of society, including way products are conceived and managed. AI-powered tools and technologies have the potential to revolutionize the way product managers work by impacting every stage of the product life-cycle, and it is already a reality in bringing very specific capabilities for development, marketing, sales and support. For example, AI facilitates rapid prototyping and refinement, expediting the development cycle and enhancing product adaptability [12, 17, 18]. Among the various activities related to software product management, there a set of them are under product manager primary responsibility, like Product Strategy.

Startups face challenges adopting established practices due to limited resources, a need for agility, and a culture of experimentation.

The normal standard when you want to use a established practice is to evaluate maturity, and decide where to invest and then train, up skill, etc. There are lots of references of how to evaluate maturity. As an evolving role of product managers in the age of AI and how AI is reshaping product development practices, it makes it harder to make this process of defining and evaluation as a continue in a normal fashion.

The project will involve a comprehensive literature review and doing a case studies to develop a framework to assess the potential impact of AI on a startup’s software product management practices, specifically during the phase of optimizing and scaling these practices, that will enable startup founders and leaders, on how to take advantage of what’s available to improve their product performance, outperform competitors, and not loosing focus.

What are the challenges of Software Product Management? Startup don’t have time … their experimentation is towards the product itself… [Startup information here from Book] This research project aims to explore how can AI in education, focusing on the potential benefits, risks, and challenges associated with its implementation. The project will involve a comprehensive literature review, empirical research, and case studies to analyze the impact of AI on students, teachers, and the educational ecosystem as a whole.

Iteration 2

1/ How to identify challenges the company is facing, that is related or can be solved by product management practices? (WHAT) 2/ Analise suitable tools (Generic or specialty) can help the company to achieve it’s results (What is available in the market) 3/ Run experiment to check if AI is producing benefits out of (We do it better, faster, with the tools)

Example:

AI can complement/improve/replace
AI can complement/improve/replace any function in a company**
the main core is identifying opportunities, applying the tool, observing how it works and iterating

Objective FEDS: a Framework for Evaluation in Design Science Research

R2D2 how to write a case study using design science to evaluate a tool effectiveness

To write a case study that evaluates a tool’s effectiveness using design science, you need to follow these steps:

1. Context and Problem:

Identify the Problem: Clearly articulate the real-world problem the tool addresses.
Provide Background: Offer context on the problem and its significance, citing relevant literature.
State Research Objectives: Define the specific research questions the case study aims to answer, typically centered around the effectiveness and impact of the tool.

2. Tool Design and Development:

Describe the Artifact: Explain the tool or artifact’s key features, functionality, and design principles in detail.
Design Science Process: Outline the steps involved in designing and developing the tool, linking it to design science principles like relevance, rigor, and design evaluation.
Implementation Details: Describe how the tool was implemented or deployed in the real-world context.

3. Case Study Methodology:

Research Design: Explain the overall research design used for the case study, including the chosen method(s) of evaluation.
Data Collection: Detail the types of data collected (quantitative and qualitative) and the tools or techniques used.
Data Analysis: Explain how the data was analyzed to draw conclusions about the tool’s effectiveness.

4. Results and Evaluation:

Present Findings: Clearly present the results of the data analysis, using tables, charts, or other visualizations where appropriate.
Evaluate Effectiveness: Discuss how the findings demonstrate the tool’s effectiveness (or lack thereof) in addressing the identified problem.
Address Research Questions: Explicitly answer each of the research questions posed at the beginning of the case study.

5. Discussion and Conclusion:

Interpret Findings: Interpret the meaning of the results in the broader context of the problem and existing solutions.
Limitations: Acknowledge the limitations of the study and suggest avenues for future research.
Contributions: Highlight the theoretical and practical contributions of the study to the field.
Conclusion: Summarize the main findings and implications, emphasizing the tool’s impact.

Additional Considerations:

User-Centric Approach: Highlight user feedback and experiences in the design, implementation, and evaluation phases.
Iterative Design: If the tool underwent iterative design processes, document these and their impact on the final artifact.
Comparative Analysis: Where possible, compare the tool’s performance with existing alternatives or baseline measures.
Ethical Considerations: Address any ethical concerns related to data collection or the use of the tool.

Key Points to Remember:

Rigor: Maintain scientific rigor throughout the design, development, and evaluation process.
Clarity: Write in clear and concise language, making complex concepts accessible to a broad audience.
Visuals: Use visuals to enhance understanding and showcase data-driven results.
Impact: Focus on demonstrating the tool’s real-world impact and potential benefits.

By carefully documenting the process and outcomes of the tool’s design and evaluation, you can create a compelling case study that contributes to the advancement of both design science and the specific domain the tool addresses.

R2D2 how about design science to evaluate a tool?

Certainly, design science is widely used to evaluate tools as part of the iterative design and development process. Here’s how:

1. Goal-Oriented Evaluation:

Design science research typically begins with identifying a specific problem or need. This provides a clear goal for tool evaluation.
The evaluation focuses on assessing whether the designed tool effectively addresses the identified problem and achieves the intended goals.

2. Multi-faceted Evaluation:

Design science encourages a multi-faceted evaluation approach to assess the tool from various angles. This may include:
- Functionality: Does the tool perform as intended and provide the necessary features to address the problem?
- Usability: Is the tool easy to use and understand for its target users?
- Efficiency: Does the tool improve efficiency or productivity in completing relevant tasks?
- Effectiveness: Does the tool lead to improved outcomes or results in the real-world context?
- Relevance: Does the tool remain relevant and adaptable to evolving needs and contexts?

3. Iterative Evaluation:

Design science research is inherently iterative. Evaluation results inform subsequent iterations of tool design and development.
The tool is continuously refined based on feedback and insights gathered from evaluation.

4. Diverse Evaluation Methods:

Design science research embraces a variety of evaluation methods tailored to the specific tool and its context. Common methods include:
- User testing and feedback: Gathering input from real users about their experience and the tool’s effectiveness.
- Expert reviews: Seeking insights from domain experts about the tool’s design and functionality.
- Controlled experiments: Conducting experiments to compare the tool’s performance against existing solutions or baseline measures.
- Simulations: Using simulations to evaluate the tool’s behavior and potential impact in different scenarios.
- Case studies: Investigating how the tool is used and its effects in real-world situations.

5. Rigor and Validity:

Design science research emphasizes rigor and validity in evaluation to ensure reliable and meaningful results. This includes:
- Clearly defined evaluation criteria and metrics.
- Appropriate sampling and data collection methods.
- Careful analysis and interpretation of results.

Examples:

A research team develops a new educational software tool. They use design science to evaluate its effectiveness by conducting user tests with students and teachers, gathering feedback, and comparing learning outcomes with traditional methods.
A company designs a new project management tool. They employ design science to evaluate its impact on team productivity and project success rates through controlled experiments and case studies.

Key Takeaway:

Design science provides a robust framework for evaluating tools throughout their development lifecycle.
It focuses on goal-oriented, iterative, and multi-faceted evaluation to ensure the designed tool is effective, usable, and relevant in addressing the identified problem.

Remember, the specific evaluation methods and criteria will vary depending on the tool and its purpose. The key is to align the evaluation with the research goals and use a combination of approaches to gain a comprehensive understanding of the tool’s performance and potential impact.

Let me know if you have any further questions or would like to explore this topic in more depth!

R2D2 Title and Abstract

a title research paper case study of using tools to help a startup to increase software product management impact using AI Tools on software product management activities.

Show drafts

Certainly, here are a few title options for a research paper or case study exploring how AI tools can assist startups in enhancing their software product management impact:

Concise Titles:

AI-Powered Product Management: A Startup Case Study
Boosting Startup Impact with AI Product Management Tools
AI Tools for Agile Product Management in Startups

More Descriptive Titles:

Leveraging AI Tools to Amplify Software Product Management Impact in Startups: A Case Study
Exploring the Role of AI in Enhancing Software Product Management Activities for Startups
A Case Study on the Application of AI Tools in Streamlining Software Product Management Processes for Startups

Titles Highlighting Specific AI Applications:

Harnessing the Power of AI for Data-Driven Product Decision-Making in Startups
AI-Enabled Customer Insights and their Impact on Product Development in Startups
Automating Routine Product Management Tasks with AI: A Startup Perspective

Remember to consider the specific focus of your research and the target audience when finalizing the title.

Please let me know if you have any other questions or requests.

profile picture

how about a reference abstract

Show drafts

Here’s a reference abstract for a research paper exploring the use of AI tools to improve software product management in startups:

Abstract:

In today’s rapidly evolving technological landscape, startups face immense pressure to deliver innovative software products efficiently and effectively. This research investigates the potential of Artificial Intelligence (AI) tools to augment software product management activities and enhance overall product impact within a startup environment. Through a comprehensive case study approach, we examine how a specific startup leveraged various AI-powered tools to streamline product decision-making, optimize resource allocation, and improve customer engagement. The findings reveal that AI can play a pivotal role in enabling startups to navigate complex product management challenges, achieve data-driven insights, and ultimately accelerate their growth trajectory. This study contributes to the growing body of knowledge on AI applications in software product management and offers practical implications for startups seeking to harness AI’s potential for competitive advantage.

Key Points Highlighted in this Abstract:

Focus on startups and their unique challenges in software product management
Explores the use of AI tools to enhance various product management activities
Employs a case study methodology to provide real-world insights
Highlights the positive impact of AI on product decision-making, resource allocation, and customer engagement
Underscores the potential of AI to drive startup growth and success

Please feel free to adapt or modify this reference abstract to align with the specific focus and findings of your research.

Let me know if you have any other questions or requests.

Iteration 1

Feedbacks Iteration 1

Hara

Dear Vitor,

Thanks for sharing your research plan and the first pilot.

As I understand there are two phases to the study:

a diagnostic assessment of the company’s PM practices
Recommendations on available and mature AI tools .

for #1 do you have any specific assessment method /tools?

For #2 have you short listed any COTS (commercially available Off-the-shelf) tools ?

Best Regards,

Hara

Hans

Dear Vitor,

Your outline looks interesting. Here is some food for thought:

I always recommend to my clients that they think about what they want to achieve, and how they want to achieve that, before they select a tool. When you start with a tool, the tool defines the what and how for you. That may look attractive to an inexperienced person, and the results feel like progress. But these results may not be optimal or even reasonable for the company from an outside perspective.
The question is how you can include that outside perspective in your research.

Best regards, Hans-Bernd

Jan

Dear Vitor,

Thank you for your email and suggested research topic. I am SUPER interested in seeing how AI can complement/improve/replace any function in a company, including product management. So, I think your suggestion is a valid one. I am a little less sure about the established practices versus non-established practices as I believe even the company that you profiled actually does product management, but simply has not named the founder doing the work as the product manager.

As to the research method, I think you’re spot on: the main core is identifying opportunities, applying the tool, observing how it works and iterating. In addition to action research, you could also read a bit about Design Science where the notion of iterative solution development is well established.

What is the next step and when do you think you want to get back to us?

Best, Jan

Helena

Dear Vitor,

I read your proposal and I fully agree with the feedback from Jan (below).

One reflection I had when reading your text, and somewhat associated with what Jan highlights, is the definition of a company having “established practices” or not.

Who decides whether that is the case or not? Is it you who perceive the case company as being without formal PM practices (based on your own experience) or is it something you know/heard from people working at the company? And where is the line between having “no practices” and having another role taking care of/doing the PM tasks?

I love the topic and the approach, and the only reason for sharing the above concern is to avoid pre-defined assumptions that might read as you already know the case before studying it.

Regards, Helena

Email

Initial Thoughts on First Paper and Overall PhD Direction

Dear Jan and Helena,

I hope you both had a wonderful and rejuvenating vacation!

I’ve been giving a lot of thought to our first paper, and I’d like to share my initial idea with you. I propose conducting an action research study on how AI tools can enhance software product management within an organization that currently lacks established practices in this area.

For the first case study, I’m considering a Brazilian FinTech company (profile attached). They don’t have a formal software product management function in place; currently, one of the founders fills that role. They are preparing for rapid growth, with 120 customers today and a goal of reaching 2,000 customers within the next two years.

Looking at the broader phd dissertation, I envision a series of case studies, varying the stage of the organization (if it’s a startup) and its size. The focus would remain on organizations without established software product management practices. Potentially, we could conduct follow-up research with organizations that do have established software product management.

From an action research methodology perspective, I’m thinking of the following steps:

Conduct an initial diagnosis of their current practices.
Identify and prioritize opportunities where software product management, in conjunction with AI Tools, can add value.
Guide and support the actual implementation of these practices.
Measure and analyze the results.

What are your initial thoughts on this direction?

Best regards, VM

Origin R2D2: escrever em inglês, para uma dupla de orientadores, um email com os seguintes pontos:

Espero que vocês tenham tido férias excepcionais
Fiquei com a responsabilidade de pensar no primeiro artigo a ser escrito
A ideia do artigo é a seguinte: fazer uma pesquisa ação sobre como ferramentas de AI podem aprimorar a gestão de produtos de software em uma organização que não tem esta prática estabelecidada.
Primeiro estudo de caso seria uma FinTech Brasileira (perfil em anexo) que eles não tem uma prática estabelecida de Software Product Management - Basicamente um dos founders que desempenha o papel. Eles estão em uma de prepação para um crescimento acelerado, hoje tem 120 clientes, e eles tem a meta de atingir 2000 clientes nos proximos dois anos
Pensando no doutororado como um todo, penso em uma série de estudos de caso, variando o estágio da organização, caso seja uma startup, e variando o tamanho da organização, mas sempre buscando organizações que não tem Software Product Management estabelecido. E possilvemente, depois, fazer uma pesquisa com organizações que tem software product management estabelecido.

O que vocês acham?

Do ponto de vista de metodologia da pesquisa ação, eu imaginei ter um diagnóstico inicial de como eles fazem, identificar e priorizar as oportunidades onde software product management + AI podem, orientar e acompanhar a implementação efetiva e mensurar os resultados.

Research Project Briefing Template

1. Introduction

The rapid advancement of artificial intelligence (AI) is transforming various aspects of society, including the realm of education. AI-powered tools and technologies have the potential to revolutionize teaching and learning practices, offering personalized experiences, automating tasks, and providing valuable insights into student progress. However, the integration of AI in education also raises ethical considerations and challenges that need to be carefully addressed.

This research project aims to explore the ethical implications of using AI in education, focusing on the potential benefits, risks, and challenges associated with its implementation. The project will involve a comprehensive literature review, empirical research, and case studies to analyze the impact of AI on students, teachers, and the educational ecosystem as a whole.

2. Research Questions

The following research questions will guide this project:

What are the potential benefits and risks of using AI in education?
How can AI be used to enhance teaching and learning practices while mitigating ethical concerns?
What are the key ethical considerations that educators and policymakers need to address when implementing AI in education?
How can AI be integrated into education in a way that promotes equity, fairness, and inclusivity?
What are the long-term implications of AI in education for students, teachers, and society?

3. Literature Review

The literature review will cover a range of topics related to AI in education, including:

The history and evolution of AI in education
Different types of AI applications in education (e.g., personalized learning, intelligent tutoring systems, automated grading)
Ethical frameworks and guidelines for AI in education
Empirical studies on the impact of AI on learning outcomes, student engagement, and teacher practices
Case studies of successful and unsuccessful AI implementations in education

4. Methodology

This research project will employ a mixed-methods approach, combining qualitative and quantitative research methods. The following methods will be used:

Literature review: A comprehensive review of academic articles, reports, and other relevant literature to understand the current state of knowledge on AI in education.
Empirical research: Surveys, interviews, and observations to gather data on the experiences and perceptions of students, teachers, and administrators regarding AI in education.
Case studies: In-depth analysis of specific educational institutions or programs that have implemented AI to understand the factors that contribute to successful or unsuccessful outcomes.

5. Timeline

The research project is expected to be completed within a timeframe of [Number] months. The following is a tentative timeline:

Month 1-2: Literature review and development of research instruments
Month 3-4: Data collection (surveys, interviews, observations)
Month 5-6: Data analysis and interpretation
Month 7-8: Report writing and dissemination of findings

6. Deliverables

The following deliverables are expected from this research project:

A comprehensive research report detailing the findings of the study
A presentation of the research findings at a relevant academic conference
A publication of the research findings in a peer-reviewed academic journal

7. Budget

The estimated budget for this research project is [Amount]. The budget will cover expenses related to:

Literature acquisition
Research materials and equipment
Travel expenses for data collection and conference attendance
Publication fees

8. Ethical Considerations

9. Conclusion

Quartz 4

Explorer

02 Paper Proposal - FinX

About the company

Key feedbacks and thoughts

Research Focus

Iteration 4

R2D2 Proposal

Iteration 3

Submitted

Complete text

Pieces - Maturity

Pieces - Others

Iteration 2

R2D2 how to write a case study using design science to evaluate a tool effectiveness

R2D2 how about design science to evaluate a tool?

R2D2 Title and Abstract

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Research Project Briefing Template

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