Designing for Agency and Affect: Constructive Framing in AI-Generated News Podcasts Reduces Negative Emotion and Enhances Self-Efficacy

The growing prevalence of AI-generated media, particularly podcasts, calls for closer examination of how algorithmic framing shapes audience perception and affective states. As an auditory news format, podcasts offer a distinctive context in which framing effects may be amplified yet remain insufficiently examined. Established research in journalism demonstrates that negative framing, while often enhancing engagement, tends to heighten emotional arousal and cognitive bias. In AI-generated news, these tendencies risk being amplified by large-scale automated production and optimization. This study investigates listener responses to news podcasts generated by large language models (LLMs), comparing the effects of constructive versus non-constructive framing on emotional states and self-efficacy. We operationalize non-constructive framing as narratives that emphasize conflict, failure, and unresolved consequences without offering pathways for resolution, whereas constructive framing systematically integrates solution-oriented perspectives, future-oriented trajectories, and cues that foreground individual or collective agency. We developed a generative podcast pipeline that integrates prompt engineering and text-to-speech (TTS) synthesis to produce paired podcast versions from identical news content. A mixed-methods empirical study ( N = 65) compared the two framing strategies in terms of their effects on emotional response and self-efficacy. The results showed that constructive framing led to a significant reduction in negative emotions compared to non-constructive framing. Furthermore, constructive framing enhanced listeners’ perceived self-efficacy in contexts where the news topics afforded interpretable and actionable forms of agency, highlighting how framing interacts with topic characteristics to shape motivational outcomes. By extending established framing-effect theories into the domain of AI-generated auditory news, this work provides empirical insights into how framing mechanisms in AI-generated media shape user experience and well-being, and inform the design of AI-generated media systems that balance engagement optimization with ethical considerations of emotional impact and agency support within human–computer interaction.