The　recent　advancements　in　artificial　intelligence　have　brought　about　significant　changes　in　education.　In　the　context　of　intelligent　campus　development,　target　detection　technology　plays　a　pivotal　role　in　applications　such　as　campus　environment　monitoring　and　the　facilitation　of　classroom　behavior　surveillance.　However,　traditional　object　detection　methods　face　challenges　in　open　and　dynamic　campus　scenarios　where　unexpected　objects　and　behaviors　arise.　Open-World　Object　Detection　(OWOD)　addresses　this　issue　by　enabling　detectors　to　gradually　learn　and　recognize　unknown　objects.　Nevertheless,　existing　OWOD　methods　introduce　two　major　uncertainties　that　limit　the　detection　performance:　the　unknown　discovery　uncertainty　from　the　manual　generation　of　pseudo-labels　for　unknown　objects　and　the　known　discrimination　uncertainty　from　perturbations　that　unknown　training　introduces　to　the　known　class　features.　In　this　paper,　we　introduce　a　Parallel　OWOD　Framework　with　Uncertainty　Mitigation　to　alleviate　the　unknown　discovery　uncertainty　and　the　known　discrimination　uncertainty　within　the　OWOD　task.　To　address　the　unknown　discovery　uncertainty,　we　propose　an　objectness-driven　discovery　module　to　focus　on　capturing　the　generalized　objectness　shared　among　various　known　classes,　driving　the　framework　to　discover　more　potential　objects　that　are　distinct　from　the　background,　including　unknown　objects.　To　mitigate　the　discrimination　uncertainty,　we　decouple　the　learning　processes　for　known　and　unknown　classes　through　a　parallel　structure　to　reduce　the　mutual　influence　at　the　feature　level　and　design　a　collaborative　open-world　classifier　to　achieve　high-performance　collaborative　detection　of　both　known　and　unknown　classes.　Our　framework　provides　educators　with　a　powerful　tool　for　effective　campus　monitoring　and　classroom　management.　Experimental　results　on　standard　benchmarks　demonstrate　the　framework＇s　superior　performance　compared　to　state-of-the-art　methods,　showcasing　its　transformative　potential　in　intelligent　educational　environments.