The collaboration between humans and artificial intelligence (AI) holds the promise of achieving superior outcomes compared to either acting alone. Nevertheless, our understanding of the conditions that facilitate such human-AI synergy remains limited. A recent meta-analysis showed that, on average, human-AI combinations do not outperform the better individual agent, indicating overall negative human-AI synergy. We argue that this pessimistic conclusion arises from insufficient attention to human learning in the experimental designs used. To substantiate this claim, we re-analyzed all 74 studies included in the original meta-analysis, which yielded two new findings. First, most previous research overlooked design features that foster human learning, such as providing trial-by-trial outcome feedback to participants. Second, our re-analysis, using robust Bayesian meta-regressions, demonstrated that studies providing outcome feedback show relatively higher synergy than those without outcome feedback. Crucially, when feedback is paired with AI explanations we tend to find positive human-AI synergy, while AI explanations provided without feedback were strongly linked to negative synergy, indicating that explanations are useful for synergy only when humans can learn to verify the AI's reliability through feedback. We conclude that the current literature underestimates the potential for human-AI collaboration because it predominantly relies on experimental designs that do not facilitate human learning, thus hindering humans from effectively adapting their collaboration strategies. We therefore advocate for a paradigm shift in human-AI interaction research that explicitly incorporates and tests human learning mechanisms to enhance our understanding of and support for successful human-AI collaboration.

翻译：人类与人工智能（AI）的协作有望实现优于任何一方单独行动的结果。然而，我们对促成此类人机协同的条件理解仍然有限。近期一项荟萃分析表明，平均而言，人机组合的表现并未超越更优的个体智能体，这揭示了整体上的人机负协同效应。我们认为，这一悲观结论源于实验设计中未充分关注人类学习过程。为证实这一观点，我们重新分析了原始荟萃分析中包含的全部74项研究，并得出两项新发现：首先，多数先前研究忽视了促进人类学习的设计要素，例如向参与者提供逐次试验的结果反馈；其次，我们通过稳健的贝叶斯元回归分析表明，提供结果反馈的研究显示出相对更高的协同效应。关键的是，当反馈与AI解释相结合时，我们倾向于观察到正向的人机协同，而仅提供AI解释却无反馈的研究则与负协同效应显著相关，这表明解释仅当人类能通过反馈学习验证AI可靠性时才对协同有益。我们得出结论：当前文献低估了人机协作的潜力，因其主要依赖未能促进人类学习的实验设计，从而阻碍人类有效调整协作策略。因此，我们主张在人机交互研究领域进行范式转变，明确纳入并测试人类学习机制，以深化对成功人机协作的理解与支持。