Over the past few weeks, I have participated in a series of class debates exploring how technological developments may **shape society over the next decade. These debates required students to examine affirmative and negative perspectives on emerging technologies, economic structures and social change. I participated in three debates, representing the negative team in two and the affirmative team in one. I also took part in a critical observation activity, which allowed me to explore alternative interpretations of debate motions and consider how different perspectives can influence an argument. This reflection examines my participation in each debate, the development of my research and communication skills, and how the framing of arguments shifted throughout the discussions.

My first debate was the negative argument of “Over the next 10 years, creators will increasingly design for platform systems rather than for audiences.” My initial responsibility was to collaborate with my teammates and establish our central arguments. I assumed the role of primary spokesperson, interpreting research and presenting arguments to the affirmative. Our position emphasised the continued importance of audiences in shaping creators’ career opportunities, educational outcomes and content strategies. We argued that although algorithms influence content distribution, audience engagement remains fundamental to the success of digital creators. In contrast, the affirmative team maintained that algorithms increasingly drive audience distribution systems as the primary algorithm.

An observer shared an external source discussing TikTok’s emphasis on content quality rather than quantity. This evidence strengthened our argument by suggesting that audience preferences and the value of content remain significant. Additionally, I mentioned an impromptu argument about social media users who unintentionally achieve viral success, using “Facebook Mums” as an example. This point was not source-based research, and I found it difficult to substantiate my claim and respond confidently to the affirmative team’s rebuttals.

The second debate examined the motion, “Over the next 10 years, AI will concentrate economic and cultural power in a small number of firms more than it will distribute new capabilities to individuals.” I represented the negative team again but was more focused on researching credible sources and developing supporting evidence.

Our central argument concerned the increasing accessibility of artificial intelligence and its potential to distribute technological capabilities beyond large corporations. We explored how AI can reduce the financial and technical barriers to entrepreneurship, enabling individuals and smaller businesses to develop products and services. We also considered how smaller businesses can use AI to improve operational efficiency, allowing them to compete with larger organisations who need to cut employees due to AI.

Conversely, the affirmative team argued that economic and cultural power would become concentrated among limited corporations controlling AI models. This debate highlighted the distinction between access to technology and ownership of the systems that support it.

For my third debate, I represented the affirmative team in response to the motion, “Over the next 10 years, declining economic security and degrading information systems will increase the importance of belief systems as sources of identity, trust, meaning and social order.” My primary responsibility was researching evidence and identifying relevant examples to support our position.

Our team interpreted belief systems primarily through the lens of religious traditions, examining their significance in regions experiencing economic insecurity and limited social welfare. Research suggested an association between weaker social welfare systems and higher levels of religiosity, providing a foundation for our argument that belief systems may offer individuals stability, community and meaning during periods of uncertainty.

The negative team challenged our position by highlighting declining religious affiliation among younger generations and questioning whether belief systems would become a priority during an economic or social emergency. These counterarguments encouraged me to recognise the limitations of interpreting belief systems exclusively through religion.

In addition to participating in three debates, I critically observed a debate which explored “Over the next 10 years, the biggest competitive advantage for online businesses will be reducing the effort required for users to switch between services.” One source examined how simplicity and convenience can create competitive advantages for online businesses, presenting ideas that could be interpreted from both affirmative and negative perspectives. Contributions from other observers further expanded my understanding by introducing examples from music streaming and digital services, including Spotify. This experience demonstrated how a single debate motion can be interpreted through different industry contexts and analytical frameworks.

Reflecting on my overall participation, I felt most confident during the first debate, as I had established a clear argument and was able to communicate persuasively. However, presenting research gathered by online teammates was challenging, particularly when their contributions were provided as brief notes without sufficient explanation. Across all three debates, establishing clear definitions proved valuable in maintaining a consistent argument and preventing misunderstandings between opposing perspectives.

In future, I would establishing a more structured opening statement, clearly outlining key arguments to minimise pauses and improve the organisation of evidence. Condensing research into concise speaking notes would also allow me to respond more effectively to opposing arguments. While AI tools may assist with organising and summarising research, reviewing their outputs critically remains essential to ensure accuracy and retain relevant evidence.

Reference list

Adebayo, KS 2025, ‘From prompts to products: The business of no-code AI is booming’, Forbes, 25 July.

Amoils, G 2026, Australia’s hottest job ad isn’t on LinkedIn, news.com.au, news.com.au — Australia’s leading news site for latest headlines.

Australia’s artificial intelligence ecosystem: Growth and opportunities | national AI centre 2026, Ai.gov.au.

Guardian staff reporter 2025, Four alternatives to spotify: Swapping is easier than you think, the Guardian, The Guardian.

Joshi, S 2026, Why website simplicity is becoming a competitive advantage in 2026, Funic Tech.

Marr, B 2024, The biggest technology trends in the next 10 years, Forbes.

Sharma, Y 2026, At least 23,000 jobs in tech have been lost to AI this year. Experts say worse is still to come, Nine.com.au.