Key points
- Algorithms feed us what we already believe, eroding the perspective-taking collective intelligence needs.
- A chatbot built to disagree, refusing yes-or-no answers, drew most students into reasoning and argument.
- Dialogic AI could provide early training for the civic deliberation that polarised societies need.
Co-authored by Aoife Ní Chíobháin, Ceolla Dillon O’Rourke, and Michael Hogan
The saying “There is strength in numbers” is not only a truism but also one of the core tenets at the heart of collective intelligence (CI) as a concept. To make decisions, come to conclusions, and solve problems on a large scale, we all benefit from each other’s collective wisdom, insight, and experience. Yet despite no shortage of such problems demanding collective wisdom, it somehow feels as though societies, countries, and even governments are more divided than ever. Qureshi and colleagues (2020) argue that division and polarisation in society are influenced in part by social media. They point to “social media-induced polarisation” (SMIP) as a key driver of negative influence—a phenomenon grounded in “anti-social media[’s]” (Tang, 2025) algorithmic blueprint (Qureshi and Bhatt, 2024).
Scrolling through your feed, you drown in a flood of posts the algorithm has cherry-picked just for you—to match your interests, confirm your beliefs, and keep you scrolling, liking, never questioning your perspectives. But how does this impact us on a broader, societal level? These algorithms create online echo chambers that perpetuate misinformation, mistrust of others with different perspectives, and hate-laced comments teeming with finger-pointing (Qureshi and Bhatt, 2024). This dynamic is chiselling away at our CI capabilities, at our fundamental ability to get along with one another, embrace different perspectives, and operate effectively as an interdependent collective. But what if the algorithms changed from “anti-social” to “pro-social”? What if we were to focus on building CI capabilities and treat this as an important goal for society, as part of a broader education system design? What if we were to witness a shift from our current monologic feeds to dialogic, perspective-sharing ones? What might this achieve? By demonstrating how generative artificial intelligence (GenAI) can enhance learners’ awareness and consideration of multiple (novel) perspectives, a recent study by Tang and Putra (2026) provides us with some answers to these questions.
The Inspiration
Tang and Putra designed their unique GenAI chatbot, the Dialogic Science Teacher (DST), by reference to Bakhtin’s (1981) theory of heteroglossia. In this theory, Bakhtin proposes that every utterance is a simultaneous response to previous utterances and an anticipation of future utterances. Therefore, according to Bakhtin, meaning-making is an inherently mercurial, ever-evolving, dialogic consequence of a multicultural melting pot of voices. Tang and Putra believe that this dialogic symphony is what builds GenAI’s extensive knowledge base. To leverage ChatGPT’s heteroglossic fluency, Tang and Putra piggyback off the software’s accessible “MyGPTs” feature, programming it to perform as a dialogic partner to students.
Supporting Dialogue
The DST was tested on 21 students across two Indonesian high schools. Students used the programme to aid revision of science topics including electromagnetism, chemical bonding, and HIV disease. To stimulate dialogue, the DST was programmed to ask an opening question containing two answer options, from which the students had to choose one and defend their choice through dialogue. The software facilitated thoughtful exchanges via reverse prompting and Socratic questioning techniques to guide students’ dialogue and thought processes. The DST also played devil’s advocate, presenting its users with constructive criticism and multiple perspectives to prompt questioning, discussion, reflection and critical engagement with material. It followed a precise rulebook: never accept a yes-or-no answer, always play devil’s advocate, and never hand over an answer when asked what it thought. Tang and Putra then used the student-AI chatlogs as a rich source of qualitative data, applying thematic analysis techniques to identify seven recurring patterns of speech: perspective-taking, reasoning, arguing, creative thinking, clarifying, elaborating, and instructing.
Outcome Manifestation
While 29 percent of students showed minimal engagement with the DST, engaging in little-to-no dialogic moves, overall, the study showed the DST to be successful in facilitating dialogic interaction among users. Seventy-one percent of interactions from students in School 1 and 59 percent from School 2 displayed dialogic characteristics, with 62 percent of students’ conversations containing between three and four core dialogic moves—perspective-taking, reasoning, arguing, and creative thinking. Samples of discussions between students and the DST provide some thought-provoking qualitative data. Take, for example, the student Alena, who, following her engagement with the DST, gained several new perspectives from which to view a chemical bonding problem. These novel perspectives encouraged Alena to engage critically with the solution she had originally proposed, in turn enriching her understanding of the problem, leading her to consider novel solutions. Another fascinating case is that of Patricia, a student whose attitude toward HIV treatment following 25 exchanges with the DST shifted from “No…we do not provide the cure,” to “Then we treat the HIV infected people, but focus more on prevention.” Patricia’s case is particularly interesting: HIV, like many sources of online conflict, is a topic that comes with personal, emotional, moral, and political strings attached. Her exchanges with the DST illustrate how GenAI, when programmed appropriately, can facilitate perspective-taking and mindset shifts even in the most adamant of people.
Limitations and Implications
The study, consisting of only 21 participants from a distinct socio-cultural context, produced findings that, while detailed and persuasive, are far from generalisable to worldwide populations. However, that isn’t to say that Tang and Putra’s study doesn’t open exciting new avenues for future research. Take, for example, the work of Audrey Tang—Taiwan’s former Digital Minister. Tang’s focus is on building radical collaboration between AI and humans to facilitate CI and community engagement. Her work involves application and iteration of civic technology tools, namely Polis, to solve community issues. Polis utilises large-scale group collaboration to bridge polarisation between disagreeing factions by amplifying the statements and motions all parties support, rather than those that split them.
What Polis and the DST both provide is a dialogic environment where multiple perspectives and voices can surface and interact to navigate where their middle ground lies. Imagine a classroom where this progression is deliberate: students first practising perspective-taking and reasoning one-to-one with classmates and with something like DST, then carrying those same habits into Polis-style group deliberation on real issues affecting their school or community. This isn’t just about better revision tools—it’s about treating dialogic AI as early training for the collective sense-making societies need if we are to address the many problems we face together.
