In this section
@article{Cutting:2023:10.1098/rsos.220274,
author = {Cutting, J and Deterding, S and Demediuk, S and Sephton, N},
doi = {10.1098/rsos.220274},
journal = {Royal Society Open Science},
pages = {1--15},
title = {Difficulty-skill balance does not affect engagement and enjoyment: a pre-registered study using artificial intelligence-controlled difficulty},
url = {http://dx.doi.org/10.1098/rsos.220274},
volume = {10},
year = {2023}
}
TY - JOUR
AB - How does the difficulty of a task affect people's enjoyment and engagement? Intrinsic motivation and flow theories posit a 'goldilocks' optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a two-player tactical game test suite with an artificial intelligence (AI)-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n = 311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue.
AU - Cutting,J
AU - Deterding,S
AU - Demediuk,S
AU - Sephton,N
DO - 10.1098/rsos.220274
EP - 15
PY - 2023///
SN - 2054-5703
SP - 1
TI - Difficulty-skill balance does not affect engagement and enjoyment: a pre-registered study using artificial intelligence-controlled difficulty
T2 - Royal Society Open Science
UR - http://dx.doi.org/10.1098/rsos.220274
UR - https://www.ncbi.nlm.nih.gov/pubmed/36756072
UR - https://royalsocietypublishing.org/doi/full/10.1098/rsos.220274
VL - 10
ER -
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