Augmenting Human Intelligence – How Generative AI Conversational Audio Extends Human Dialogue

March 18, 2025

In 2017, my friend and fellow mapmaker Christine Capra from consultancy company Greater than the Sum, interviewed me about my work on participatory community network mapping as collective sensemaking. We had a blast, Christine with her incisive questions turning this into a conversation so insightful, that – instead of Christine converting it into a single blog post – she produced a full series of six blog posts – over the next few years. As Christine wrote in her concluding post -at the height of the COVID19 pandemic:

It’s been over two years since my original interview with Aldo. That one interview was so rich, it generated six blog posts and an abiding friendship. We’ve met online semi-monthly ever since that first great conversation, to talk about our projects, challenges, ideas & dreams. While the Covid 19 pandemic has made dramatic changes to our collective daily lives since that first interview, what we observe and believe about mapping feels more relevant than ever. What holds our friendship together, through all the changes, is our nearly identical professional response to this question, whose urgency is only increased due to the pandemic:

Ever since, we felt we were far from finished, but, as small business owner pressures leave both of us too little time for sustained on-topic conversations, let alone writing them up, that plan never came to be.

However, in 2025, generative AI came to the rescue. While all the buzz is about how tools like ChatGPT can make individual writing so much easier, and sometimes even too easy , there are other ways of deploying it. Instead of a human prompting a generative AI tool to write a piece from scratch we can now feed existing content to the machine to reflect on it and even turn it into a podcast: hello, NotebookLM

NotebookLM is an AI-powered tool that helps users organize and synthesize information from various sources, such as documents and web pages. It can generate summaries, study guides, and even create engaging audio discussions or podcasts based on the uploaded content, allowing users to reflect on and interact with their sources in a more dynamic way. This feature enables users to transform existing content into conversational audio formats, making complex information more accessible and engaging (see Notebook LM: A Guide With Practical Examples for a very helpful introduction if you want to get started yourself).

As an experiment, I fed NotebookLM with pdfs of the six blog posts, let it churn away for a couple of minutes, and then it came up with this “radioshow'” in which two “radio hosts” try to make sense of what participatory community network mapping is about and why it matters. My expectations were not very high, but I must say that I was in awe of how well they “understood” what Christine and I meant and even came up with very interesting interpretations that had not even crossed my mind. It definitely jogged my memory and provided me with lots of new food for thought. Judge for yourself:

Click here to listen to our artificial friends joining the chat

(At the bottom of this post I have included some additional conversational materials auto-generated by NotebookLM that are excellent starting points for further educational and research purposes: an interview timeline and a study guide)

In my field of research and practice – community informatics – the effective use of ICTs is a core topic of interest. Understanding useful and responsible applications of generative AI in general is still in its infancy, let alone how to meaningfully deploy such specialized generated conversational audio formats. However, this little experiment definitely whetted my appetite to explore further.

The output of those engaging conversational audio formats, of course, depend on the quality of the sources being provided. In our blog series case, those sources represent an extended conversation between two highly qualified experts in a domain, who, improvising, de facto outline a knowledge domain, including relevant topics, observations, and directions for future research and development. In a way, it could be seen as a high-quality “extended prompt”, setting a relevant and interesting stage for the machine to then continue the conversation, which might in turn spawn new human conversations, and so on. It is a variation of research I did two decades ago – amplified by the technology of today – with Lilia Efimova, on how growing “webs of blog conversations” can lead to ever richer and deeper collective insights (see An Argumentation Analysis of Weblog Conversations and Beyond Personal Webpublishing: An Exploratory Study of Conversational Blogging Practices).

There is – rightly – much fear of what ruthless use of AI can unleash – and sadly already is unleashing – upon humanity. However, this only makes it more urgent that we much better understand how the the two types of intelligence – human and machine – can evolve into a true hybrid collective intelligence. As we explore the potential of generative AI to enhance our collective understanding, we are reminded of Douglas Engelbart‘s vision, who already in 1962 said:

The term ‘intelligence amplification’ seems applicable to our goal of augmenting the human intellect in that the entity to be produced will exhibit more of what can be called intelligence than an unaided human could; we will have amplified the intelligence of the human by organizing his intellectual capabilities into higher levels of synergistic structuring.

This vision underscores the importance of using technology not to replace human capabilities, but to augment them. Our collective intelligence orchestra just gained an intriguing new instrument – one we’re still learning to tune and play. Like any good ensemble, the real magic will happen when we humans set the tone while these AI instruments provide the complementary rhythms and harmonies that enrich the overall composition.

Additional NotebookLM-generated conversational materials

Timeline

Here is a detailed timeline of the main events and a cast of characters from the provided
interview excerpts:

Timeline of Main Events and Concepts Discussed:

Ongoing Discussion (Referenced Throughout):

    • Participatory Community Network Mapping: The central theme of the interview series, exploring its methodology, benefits, challenges, and impact on social innovation.

    • Aldo de Moor’s Work: Aldo’s research and practice in bridging the gap between research and practice in Community Informatics, particularly through participatory mapping. His founding of CommunitySense and his roles at STARLab and Tilburg University are recurring context.

    • The Social Innovation Process Model: A six-stage model (Prompts, Proposals, Prototypes, Sustaining, Scaling, Systemic Change) developed by NESTA and referenced by Aldo as a framework where participatory network mapping can be applied.

Key Concepts and Methodological Aspects Introduced and Discussed Across the Parts:

    • Part 1 (Published Aug. 3rd, 2017):Introduction to Participatory Community Network Mapping as a crucial methodology for social innovation.

    • Emphasis on collective sensemaking as a core requirement and democratizing the creation of meaningful maps.

    • Highlighting the importance of putting projects and prototypes into context at a broader societal level.

    • Discussion of overcoming fragmentation and strengthening feedback loops for sustaining and scaling change.

    • Introduction of the idea that maps engender and capture collective conversations.

    • Framing mapping as a way to navigate complex and confusing situations.

    • Mention of using the participatory community network mapping approach to promote the sharing of social innovation lessons learned between European cities (as a future topic).

    • Part 2 (Published Aug. 14th, 2017):Exploring why participatory community network mapping “makes possible,” focusing on its ability to navigate complex terrain and capture the “what,” “whose,” and “whom” of community engagement.

    • Emphasis on sensemaking as a core function: mapping of, for, and by the community.

    • Using the metaphor of the “blind men and the elephant” to illustrate the need to see the bigger picture through diverse perspectives.

    • Highlighting “system-revealing and insight-sharing conversations” as a key outcome of mapping.

    • Discussion of “feedback loops and action learning” facilitated by the mapping process.

    • Framing mapping as a way to “find a new path forward” by revealing underlying dynamics and supporting collective sensemaking.

    • Discussing how mapping helps overcome fragmentation by revealing overlaps and connections.

    • Introduction of the European Union program and the idea of exchanging lessons learned in city network development.

    • Mention of a meeting in Barcelona with representatives from eleven European cities.

    • Part 3 (Published Sep. 11th, 2017):Focus on the power inherent in participatory community network mapping.

    • Discussion of how maps represent, create, and perpetuate power dynamics.

    • Acknowledging the potential for maps to be used for meritocracy and the challenges of navigating existing power structures.

    • Exploring how participatory mapping can address dominance and promote more equitable representation.

    • Highlighting that if used to make decisions, maps become a “representation of dominance” if not handled carefully.

    • Discussing how building distributed power bases through collaborative mapping can federate community power.

    • Mention of global lessons learned on integrating gender, nutrition, and agricultural extension (as a future topic).

    • Part 4 (Published Dec. 14th, 2017):Focus on the role of language and stories in participatory community network mapping.

    • Emphasis on language as power and its influence on how we understand and shape our realities through maps.

    • Discussion of how dominant language can open up or close down thinking and the importance of considering different languages (“interlingua”).

    • Highlighting “sharing stories to catalyze sensemaking” as a crucial aspect of the mapping process.

    • Exploring how maps and their connections form “community collaboration patterns” that trigger sensemaking.

    • Mention of Kumu’s ability to handle narrative and cultural artifacts in maps.

    • Future discussion on the process of participatory mapping in a participatory way.

    • Part 5 (Published Oct. 8th, 2018):Focus on the process of participatory community network mapping.

    • Addressing the challenges of fragmented conversations and the need for integrated sensemaking.

    • Introducing the CommunitySensor methodology, which includes phases like Scoping, Seed Mapping, Starting to Make Sense, and Embedding.

    • Detailed breakdown of Phase 1: Scoping (defining the context), including discovering conditions for common ground and ownership.

    • Discussion of Phase 2: Seed Mapping (the collaboration quickscan), focused on identifying key elements and connections.

    • Beginning to describe Phase 3: Starting to make sense (interpreting the map), emphasizing sensemaking meetings and “Gap Analysis.”

    • Part 6 (Published May 19th, 2020):Reflecting on the original discussions over two years, highlighting the impact of the initial interview and subsequent conversations, including the influence of the COVID-19 pandemic.

    • Questioning what helps communities become “greater than the sum of their parts.”

    • Emphasizing the need for new visual language and generative representations to navigate complex realities.

    • Highlighting that mapping is a process, not a product, and metrics will always be incomplete.

    • Discussing the importance of adopting the “latest glitzy technology” thoughtfully and focusing on its ability to facilitate interaction.

    • Emphasizing network development (awareness, connectivity, diversity, flows) and network capacity (overtaking or catalyzing augmentation) as key outcomes.

    • Reiterating that mapping and conversations are two sides of the same coin.

    • Focusing on collective sensemaking (shared perspectives, shared understanding, shared use of maps).

    • Concluding with the idea of keeping power and language in mind throughout the mapping process and the importance of embedding the mapping process for long-term impact.

Cast of Characters:

    • Aldo de Moor:A research consultant dedicated to bridging the gap between research and practice in Community Informatics.

    • Founder of CommunitySense, a platform for consulting work using Kumu.

    • Previously a Senior Researcher at the STARLab in Brussels, Belgium.

    • Also previously an Assistant Professor at Tilburg University in the Netherlands.

    • Has worked with a wide range of clients across education, research, government, public libraries, business, and social innovation.

    • One of the principal voices and experts discussed in the interview series.

    • Christine:The interviewer and author of the blog posts featuring the interview with Aldo de Moor.

    • Her questions and framing guide the conversation and exploration of participatory community network mapping.

    • Kumu:A mapping platform described as having a list of Kumu wizards available for consulting work.

    • Its wizards are themselves called ‘Kumu’.

    • Mentioned as a tool Aldo uses in his consulting work through CommunitySense.

    • NESTA:(No specific person mentioned) A UK-based innovation foundation.

    • Referenced for their “Social Innovation Process model” which Aldo de Moor uses as a context for participatory network mapping.

    • Angela Carter:(No specific bio provided) Quoted in Part 4 regarding the power of language and culture.

    • Ludwig Wittgenstein:(No specific bio provided) Quoted in Part 4 regarding the limits of language.

This timeline and cast of characters provide a comprehensive overview of the main topics and individuals discussed in the provided interview excerpts. The series focuses on the principles and practices of Participatory Community Network Mapping as articulated by Aldo de Moor through his conversation with Christine.

Participatory Community Network Mapping Study Guide

Key Concepts and Themes

    • Participatory Community Network Mapping (PCNM): A collaborative approach to mapping relationships and connections within a community to foster collective sensemaking and action.

    • Collective Sensemaking: The process through which a group of people collaboratively interpret and understand a situation or issue. PCNM is presented as a method to democratize the creation of meaningful mental maps of the world.

    • Social Innovation Process: The multi-stage process involving prompts, proposals, prototypes, sustaining, and scaling of innovative solutions, within which PCNM can play a role.

    • Prototypes and Projects: The unit of analysis in PCNM, emphasizing focused, project-level understanding before scaling.

    • Overcoming Fragmentation: PCNM aims to address the fragmented nature of projects and resources by revealing connections and potential synergies within a community.

    • Feedback Loops: Essential for sustaining and scaling change, PCNM can facilitate the sharing of energy, information, and results across a network.

    • Maps as Conversations: Viewing maps not just as static representations but as tools to stimulate and guide ongoing dialogue within a community.

    • Power Dynamics: An inherent aspect of mapping, requiring careful consideration of whose perspectives are included and how the map might influence power structures. PCNM can either reinforce or challenge existing dominance.

    • Language and Story: Critical elements in shaping the meaning and impact of maps. The language used and the stories shared during the mapping process are powerful.

    • Scaling and Embedding: The challenges of expanding the reach and integrating PCNM into existing community practices and systems.

    • Sensemaking as Core: PCNM is fundamentally a sensemaking activity, helping communities understand themselves and their context better.

    • System-Revealing Conversations: Maps can help set agendas for conversations that reveal the underlying dynamics and connections within a network.

    • Learning and Adaptation: The iterative nature of PCNM, where feedback and new insights lead to ongoing learning and adjustments.

    • Gap Analysis: A part of the sensemaking process that involves identifying differences in how the community sees its current state and desired future.

    • Embedding the Mapping Process: Integrating the insights and outcomes of mapping into the regular activities and decision-making of the community.

    • Metapatterns: Universal patterns in collaboration ecosystems that PCNM can help uncover and leverage.

    • Collaboration Ecosystem: The complex web of relationships, resources, and activities within a community working towards common goals.

    • Seed Mapping: The initial phase of mapping focused on capturing the “architecture,” “structure,” and key elements of a community network.

Quiz

    1. What is the primary goal of Participatory Community Network Mapping (PCNM) according to Aldo de Moor?
    2. Explain the concept of “collective sensemaking” and how PCNM facilitates it.
    3. Describe the role of “prototypes and projects” in the context of PCNM.
    4. According to the interview, how can PCNM help in “overcoming fragmentation” within communities?
    5. Why are “feedback loops” considered important in relation to PCNM and social change?
    6. In what ways can maps created through PCNM be viewed as more than just static representations?
    7. Discuss the significance of considering “power dynamics” when undertaking participatory mapping initiatives.
    8. How do “language and story” influence the process and outcomes of PCNM?
    9. What are some of the key challenges associated with “scaling and embedding” PCNM within a community?
    10. According to the interview, what is the fundamental nature of PCNM in terms of community understanding?

Quiz Answer Key

    1. The primary goal of PCNM, according to Aldo de Moor, is to bridge the gap between research and practice in the field of Community Informatics and to function as a crucial methodology for social innovation by enabling collective sensemaking and understanding of community connections and resources.
    2. Collective sensemaking is the collaborative interpretation and understanding of a situation by a group. PCNM facilitates this by providing a platform and visual representation of community networks, allowing participants to collectively identify patterns, relationships, and shared perspectives that contribute to a common understanding.
    3. In PCNM, “prototypes and projects” serve as the focused unit of analysis. Instead of immediately trying to map an entire complex system, the process often starts by examining specific projects and the networks surrounding them. This allows for a more manageable and grounded understanding of the larger community network.
    4. PCNM can help in overcoming fragmentation by making visible the existing connections and potential relationships between different projects, resources, and individuals within a community. By mapping these networks, it reveals overlaps, gaps, and opportunities for collaboration and synergy that might otherwise remain unseen.
    5. Feedback loops are crucial for sustaining and scaling change because they allow for the continuous sharing of information, energy, and learning within a network. PCNM can strengthen these loops by providing a visual and conversational platform for reflecting on actions, outcomes, and necessary adjustments, leading to more adaptive and effective community-led initiatives.
    6. Maps created through PCNM are viewed as more than static representations because they are generated through a participatory and conversational process. They embody the collective understanding and narratives of the community members involved, act as catalysts for ongoing dialogue, and evolve as the community’s understanding and relationships change.
    7. Considering power dynamics is significant in PCNM because the mapping process can inadvertently reinforce existing inequalities by prioritizing certain voices or perspectives. A conscious effort to ensure inclusivity, acknowledge different levels of influence, and design the mapping process to be equitable is essential for creating a map that truly reflects the community and empowers marginalized voices.
    8. Language and story are critical because they shape how community members understand and articulate their connections and experiences. The language used in the mapping process and the stories shared about relationships and initiatives contribute significantly to the meaning-making and the overall narrative that emerges from the map.
    9. Key challenges associated with scaling and embedding PCNM include maintaining participation and engagement over time, integrating the mapping insights into existing community structures and decision-making processes, ensuring the sustainability of the mapping effort beyond the initial phase, and adapting the methodology to different contexts and community needs.
    10. According to the interview, PCNM is fundamentally a sensemaking activity, serving as a powerful set of tools and processes that enable a community to collectively understand itself – its members, resources, relationships, and the broader context in which it operates – leading to more informed and collaborative action.

Essay Format Questions

    1. Discuss the relationship between Participatory Community Network Mapping (PCNM) and the concept of collective sensemaking. How does PCNM facilitate or enhance a community’s ability to make sense of itself and its challenges? Provide specific examples drawn from the interview excerpts to support your arguments.
    2. Analyze the role of power dynamics in the process and outcomes of Participatory Community Network Mapping. How can these dynamics influence who participates, what information is included, and how the resulting map is interpreted and used? What strategies might be employed to mitigate potential negative impacts of power imbalances in PCNM initiatives?
    3. Explore the significance of language and storytelling in Participatory Community Network Mapping. How do the words used and the narratives shared during the mapping process shape the understanding and impact of the resulting network map? Consider the implications of different linguistic choices and the power of collective storytelling in fostering community action.
    4. Evaluate the challenges and opportunities associated with scaling and embedding Participatory Community Network Mapping within a community or organization. What factors contribute to the successful integration of PCNM into ongoing practices? What obstacles might hinder its widespread adoption and long-term sustainability?
    5. Drawing on the entirety of the interview excerpts, synthesize Aldo de Moor’s vision for Participatory Community Network Mapping as a methodology for social innovation. What are the core principles, key processes, and intended outcomes of this approach? How does it differ from more traditional methods of network analysis or community assessment, and what makes it a valuable tool for addressing complex social issues?

Glossary of Key Terms

    • Participatory Community Network Mapping (PCNM): A collaborative method involving community members in the process of visually representing and analyzing their relationships, connections, and resources to foster shared understanding and action.

    • Collective Sensemaking: The process by which a group of individuals jointly interpret and create meaning from a shared experience or situation, leading to a collective understanding.

    • Community Informatics: An interdisciplinary field that studies the design, implementation, deployment, and use of information and communication technologies for community development and empowerment.

    • Kumu: A web-based platform specifically designed for visualizing complex systems and networks, often used in participatory mapping initiatives.

    • Social Network Analysis (SNA): A quantitative method for studying relationships and connections between entities (individuals, groups, organizations) within a network. PCNM is presented as a more qualitative and participatory alternative or complement to traditional SNA.

    • Maker of Meaning: A central aspect of PCNM, emphasizing the collaborative construction of shared understanding and interpretation of the mapped network.

    • Democratizing Creation of Meaningful Maps: The idea that PCNM empowers community members to actively participate in creating representations of their world, rather than relying solely on external experts or data.

    • Social Innovation Process: A framework that outlines the stages involved in developing and implementing new solutions to social problems, often involving iterative cycles of ideation, prototyping, and scaling.

    • Prototypes: Early-stage models or examples of potential solutions or initiatives, used in PCNM as a focused unit for mapping and understanding related networks.

    • Fragmentation: The state of being broken into disconnected parts or elements, a challenge that PCNM aims to address by revealing connections between disparate efforts within a community.

    • Feedback Loops: Processes within a system where the output or results of an action influence future actions, crucial for learning, adaptation, and sustainable change.

    • Empowerment: The process of gaining control or agency over one’s life and circumstances, often a goal of participatory approaches like PCNM.

    • Systemic Change: Fundamental shifts in the underlying structures, processes, and relationships within a system, often a long-term aspiration of social innovation efforts.

    • Maps as Catalysts for Conversation: Viewing network maps not merely as static depictions but as tools to initiate and guide meaningful dialogues within a community.

    • Power Dynamics: The interplay of influence and authority between different individuals or groups within a system, a critical consideration in participatory processes.

    • Language as Power: The idea that the words used to describe and frame a situation can significantly influence how it is understood and acted upon.

    • Scaling: Expanding the reach and impact of a successful initiative or approach to a wider context or population.

    • Embedding: Integrating a new practice or tool into the routine activities and structures of a community or organization to ensure its long-term use and sustainability.

    • Sensemaking: The ongoing effort to understand events, relationships, and the environment around us, particularly in ambiguous or complex situations.

    • System-Revealing: The capacity of a map or a conversation to uncover underlying structures, dynamics, and connections within a network or system.

    • Gap Analysis: The process of identifying the differences between a current state and a desired future state, often used to inform planning and action.

    • Metapattern: A recurring pattern or structure that appears across different contexts or systems, offering insights into fundamental principles of collaboration and organization.

    • Collaboration Ecosystem: The network of relationships, resources, and activities that support collaborative efforts within a community or field.

    • Seed Mapping: An initial phase of participatory mapping focused on identifying the core elements, key actors, and fundamental connections within a community network as a starting point for deeper exploration.

Posted by Aldo de Moor

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