@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}
}

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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
UR  - http://hdl.handle.net/10044/1/102741
VL  - 10
ER  - 

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