Based　on　the　self-determination　theory　(SDT),　this　evaluates　the　effects　of　generative　artificial　intelligence　(Gen-AI)　on　learning　performance　within　China＇s　education　sector,　emphasizing　the　roles　of　social　interaction,　utilitarian　benefit,　knowledge　acquisition,　and　epistemic　curiosity.　The　study　employs　a　dual　method,　using　PLS-SEM　and　fsQCA　approaches　for　data　analysis.　Data　were　collected　through　an　online　questionnaire　from　students　and　teachers　from　Chinese　institutes.　The　findings　suggest　that　students　and　teachers　have　positive　opinions　on　the　influence　of　Gen-AI　on　learning　performance　through　social　interaction　and　knowledge　acquisition.　Utilitarian　benefits　positively　mediate　the　affiliation　between　Gen-AI　and　teachers＇　learning　performance,　but　in　the　case　of　students,　they　negatively　mediate.　Furthermore,　epistemic　curiosity　acts　as　a　positive　moderator　between　Gen-AI　technologies　and　social　interaction　and　knowledge　acquisition,　but　it　has　a　negative　relationship　with　Gen-AI　technologies　and　utilitarian　benefits.　Furthermore,　the　fsQCA　analysis　reveals　robust　configurations　with　high　consistency,　explaining　learning　performance　outcomes　reliably　and　highlighting　significant　and　unique　contributions　of　specific　configurations.　The　implications　of　this　study　emphasize　how　crucial　generative　AI　technologies　are　in　educational　frameworks　to　optimize　their　potential　benefits　and　enhance　learning　outcomes　in　China＇s　quickly　changing　educational　landscape.