A qualitative research study on how Gen Alpha and Z relate to AI, and what this means for the future of digital experiences.
Client
University of Messina
Industry
Research & Academia
Year
2025
Methodology
Qualitative research
Role
Lead UX Researcher
Team
Chief Experience Officer Lead UX Researcher Research Fellow Associate Professor
Activities
Research design Semi-structured interviews Data analysis Report writing
Deliverables
Research report Academic paper contribution
A qualitative research study on how Gen Alpha and Z relate to AI, and what this means for the future of digital experiences.
Client
University of Messina
Industry
Research & Academia
Year
2025
Methodology
Qualitative research
Role
Lead UX Researcher
Team
Chief Experience Officer Lead UX Researcher Research Fellow Associate Professor
Activities
Research design Semi-structured interviews Data analysis Report writing
Deliverables
Research report Academic paper contribution
A qualitative research study on how Gen Alpha and Z relate to AI, and what this means for the future of digital experiences.
Client
University of Messina
Industry
Research & Academia
Year
2025
Methodology
Qualitative research
Role
Lead UX Researcher
Team
Chief Experience Officer Lead UX Researcher Research Fellow Associate Professor
Activities
Research design Semi-structured interviews Data analysis Report writing
Deliverables
Research report Academic paper contribution
Challenge
AI is advancing faster than our understanding of the people who use it. As digital products become increasingly AI-powered, designing experiences that truly resonate with younger generations cannot rely on assumptions alone. Notomia partnered with the Innovation Management research team at the University of Messina to investigate how Gen Alpha and Gen Z are relating to AI, what they expect from it and how their attitudes will shape the digital products of tomorrow. The research was driven by a clear strategic intent: understanding the next generation of users before their expectations become the new standard.
Challenge
AI is advancing faster than our understanding of the people who use it. As digital products become increasingly AI-powered, designing experiences that truly resonate with younger generations cannot rely on assumptions alone. Notomia partnered with the Innovation Management research team at the University of Messina to investigate how Gen Alpha and Gen Z are relating to AI, what they expect from it and how their attitudes will shape the digital products of tomorrow. The research was driven by a clear strategic intent: understanding the next generation of users before their expectations become the new standard.
Challenge
AI is advancing faster than our understanding of the people who use it. As digital products become increasingly AI-powered, designing experiences that truly resonate with younger generations cannot rely on assumptions alone. Notomia partnered with the Innovation Management research team at the University of Messina to investigate how Gen Alpha and Gen Z are relating to AI, what they expect from it and how their attitudes will shape the digital products of tomorrow. The research was driven by a clear strategic intent: understanding the next generation of users before their expectations become the new standard.
Role and process
I led the research end to end, from protocol design to final report, in collaboration with the University of Messina: - Research design: defined the research questions and built the interview framework - Fieldwork: conducted and supervised semi-structured interviews with the research sample - Analysis: contributed to the synthesis of qualitative data to identify patterns - Report writing: translated findings into a structured report, which contributed to an academic paper and book published by the University of Messina
Role and process
I led the research end to end, from protocol design to final report, in collaboration with the University of Messina: - Research design: defined the research questions and built the interview framework - Fieldwork: conducted and supervised semi-structured interviews with the research sample - Analysis: contributed to the synthesis of qualitative data to identify patterns - Report writing: translated findings into a structured report, which contributed to an academic paper and book published by the University of Messina
Role and process
I led the research end to end, from protocol design to final report, in collaboration with the University of Messina: - Research design: defined the research questions and built the interview framework - Fieldwork: conducted and supervised semi-structured interviews with the research sample - Analysis: contributed to the synthesis of qualitative data to identify patterns - Report writing: translated findings into a structured report, which contributed to an academic paper and book published by the University of Messina
Outcome
The research surfaced clear design implications for the next generation of AI-powered digital products. The findings translated into concrete design directions: interfaces that anticipate user needs rather than react to them, conversational patterns that feel natural rather than mechanical and personalisation systems that earn trust over time rather than assuming it. For product teams building AI-powered experiences, this research offers an evidence-based foundation for design decisions that go beyond trend and into user reality.
Outcome
The research surfaced clear design implications for the next generation of AI-powered digital products. The findings translated into concrete design directions: interfaces that anticipate user needs rather than react to them, conversational patterns that feel natural rather than mechanical and personalisation systems that earn trust over time rather than assuming it. For product teams building AI-powered experiences, this research offers an evidence-based foundation for design decisions that go beyond trend and into user reality.
Outcome
The research surfaced clear design implications for the next generation of AI-powered digital products. The findings translated into concrete design directions: interfaces that anticipate user needs rather than react to them, conversational patterns that feel natural rather than mechanical and personalisation systems that earn trust over time rather than assuming it. For product teams building AI-powered experiences, this research offers an evidence-based foundation for design decisions that go beyond trend and into user reality.
Main findings
Three patterns emerged consistently across the interviews: - Two generations, two different approaches: Gen Alpha engages with AI naturally, without friction or emotional attachment while Gen Z relies on it daily but questions its transparency and reliability. - Familiarity does not equal trust: Despite high adoption rates, trust in AI remains remarkably low across both groups, making it one of the central challenges ahead. - The expectation gap: Both generations feel current AI falls short of its potential. They want experiences that anticipate their needs and feel genuinely personal, not just algorithmically present.
Main findings
Three patterns emerged consistently across the interviews: - Two generations, two different approaches: Gen Alpha engages with AI naturally, without friction or emotional attachment while Gen Z relies on it daily but questions its transparency and reliability. - Familiarity does not equal trust: Despite high adoption rates, trust in AI remains remarkably low across both groups, making it one of the central challenges ahead. - The expectation gap: Both generations feel current AI falls short of its potential. They want experiences that anticipate their needs and feel genuinely personal, not just algorithmically present.
Main findings
Three patterns emerged consistently across the interviews: - Two generations, two different approaches: Gen Alpha engages with AI naturally, without friction or emotional attachment while Gen Z relies on it daily but questions its transparency and reliability. - Familiarity does not equal trust: Despite high adoption rates, trust in AI remains remarkably low across both groups, making it one of the central challenges ahead. - The expectation gap: Both generations feel current AI falls short of its potential. They want experiences that anticipate their needs and feel genuinely personal, not just algorithmically present.
Impact
Conducted in partnership with the Innovation Management research team at the University of Messina, this study combined academic rigor with a practical design purpose. A gender-balanced sample of participants aged 12 to 25, recruited across diverse Italian regions, generated findings that were both representative and actionable. What emerged is a clear set of expectations: personalisation that actually works, instant responsiveness and interfaces that feel human. For product teams, this translates into a concrete design agenda before these expectations become table stakes. The depth of this work earned it a place beyond the design industry, contributing to an academic paper and book published by the University of Messina.
Impact
Conducted in partnership with the Innovation Management research team at the University of Messina, this study combined academic rigor with a practical design purpose. A gender-balanced sample of participants aged 12 to 25, recruited across diverse Italian regions, generated findings that were both representative and actionable. What emerged is a clear set of expectations: personalisation that actually works, instant responsiveness and interfaces that feel human. For product teams, this translates into a concrete design agenda before these expectations become table stakes. The depth of this work earned it a place beyond the design industry, contributing to an academic paper and book published by the University of Messina.
Impact
Conducted in partnership with the Innovation Management research team at the University of Messina, this study combined academic rigor with a practical design purpose. A gender-balanced sample of participants aged 12 to 25, recruited across diverse Italian regions, generated findings that were both representative and actionable. What emerged is a clear set of expectations: personalisation that actually works, instant responsiveness and interfaces that feel human. For product teams, this translates into a concrete design agenda before these expectations become table stakes. The depth of this work earned it a place beyond the design industry, contributing to an academic paper and book published by the University of Messina.
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