{"id":"https://openalex.org/W7138014274","doi":"https://doi.org/10.1609/aaai.v40i38.40544","title":"Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation","display_name":"Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138014274","doi":"https://doi.org/10.1609/aaai.v40i38.40544"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i38.40544","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i38.40544","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i38.40544","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129717907","display_name":"Jaewoo Park","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jaewoo Park","raw_affiliation_strings":["Yonsei University","Yonsei University\nMathpresso"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University\nMathpresso","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010224335","display_name":"Jungyang Park","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jungyang Park","raw_affiliation_strings":["Yonsei University","Yonsei University\nMathpresso"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University\nMathpresso","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043773581","display_name":"Dongju Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongju Jang","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102568508","display_name":"Jiwan Chung","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiwan Chung","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129662614","display_name":"Byungwoo Yoo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099679","display_name":"Protein Express (United States)","ror":"https://ror.org/013vgsx18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210099679"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Byungwoo Yoo","raw_affiliation_strings":["Mathpresso"],"affiliations":[{"raw_affiliation_string":"Mathpresso","institution_ids":["https://openalex.org/I4210099679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024687495","display_name":"Jaewoo Shin","orcid":"https://orcid.org/0000-0002-6335-1292"},"institutions":[{"id":"https://openalex.org/I4210099679","display_name":"Protein Express (United States)","ror":"https://ror.org/013vgsx18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210099679"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaewoo Shin","raw_affiliation_strings":["Mathpresso"],"affiliations":[{"raw_affiliation_string":"Mathpresso","institution_ids":["https://openalex.org/I4210099679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129729752","display_name":"Seonjoon Park","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099679","display_name":"Protein Express (United States)","ror":"https://ror.org/013vgsx18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210099679"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seonjoon Park","raw_affiliation_strings":["Mathpresso"],"affiliations":[{"raw_affiliation_string":"Mathpresso","institution_ids":["https://openalex.org/I4210099679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129719106","display_name":"Taehyeong Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099679","display_name":"Protein Express (United States)","ror":"https://ror.org/013vgsx18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210099679"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taehyeong Kim","raw_affiliation_strings":["Mathpresso"],"affiliations":[{"raw_affiliation_string":"Mathpresso","institution_ids":["https://openalex.org/I4210099679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129721651","display_name":"Youngjae Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngjae Yu","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5129717907"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29477612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"38","first_page":"32664","last_page":"32672"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.47589999437332153,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.47589999437332153,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10130","display_name":"Mathematics Education and Teaching Techniques","score":0.03830000013113022,"subfield":{"id":"https://openalex.org/subfields/3304","display_name":"Education"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11516","display_name":"Visual and Cognitive Learning Processes","score":0.037300001829862595,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7181000113487244},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7067000269889832},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.6425999999046326},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5687000155448914},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.4860000014305115},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.45329999923706055},{"id":"https://openalex.org/keywords/visual-learning","display_name":"Visual learning","score":0.43059998750686646},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4133000075817108},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.40790000557899475}],"concepts":[{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7085999846458435},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7067000269889832},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.6425999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5914000272750854},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5687000155448914},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.4860000014305115},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.39489999413490295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37630000710487366},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.37599998712539673},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29030001163482666},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C13606891","wikidata":"https://www.wikidata.org/wiki/Q2623243","display_name":"Conceptual model","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C2778638050","wikidata":"https://www.wikidata.org/wiki/Q5421252","display_name":"Explanatory model","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i38.40544","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i38.40544","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i38.40544","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i38.40544","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8702167272567749,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,67,141,181,187],"rapid":[2],"advancement":[3],"of":[4,26,112,143],"mathematical":[5,164],"reasoning":[6],"capabilities":[7],"in":[8,19,35,158,168,175,196],"Large":[9],"Language":[10],"Models":[11],"(LLMs),":[12],"AI":[13,205],"systems":[14],"are":[15],"increasingly":[16],"being":[17],"adopted":[18],"educational":[20,176],"settings":[21],"to":[22,57,73,136,162],"support":[23],"students\u2019":[24],"comprehension":[25],"problem-solving":[27],"processes.":[28],"However,":[29],"a":[30,108,155],"critical":[31],"component":[32],"remains":[33],"underexplored":[34],"current":[36,133,159],"LLM-generated":[37],"explanations:":[38],"multimodal":[39,68,109,182],"explanation.":[40],"In":[41,140],"real-world":[42],"instructional":[43],"contexts,":[44],"human":[45],"tutors":[46],"routinely":[47],"employ":[48],"visual":[49,79,118,138,165],"aids,":[50],"such":[51,81],"as":[52,82,202],"diagrams,":[53],"markings,":[54],"and":[55,87,120,172,186,198],"highlights,":[56],"enhance":[58],"conceptual":[59],"clarity.":[60],"To":[61,98],"bridge":[62],"this":[63,103],"gap,":[64],"we":[65,105],"introduce":[66],"solution":[69,183],"explanation":[70,184],"task,":[71,104],"designed":[72],"evaluate":[74,99],"whether":[75],"models":[76,134,148],"can":[77],"identify":[78,137],"keypoints,":[80],"auxiliary":[83],"lines,":[84],"points,":[85],"angles,":[86],"generate":[88],"explanations":[89,174],"that":[90,124,132,180],"incorporate":[91],"these":[92],"key":[93],"elements":[94],"essential":[95],"for":[96],"understanding.":[97],"model":[100],"performance":[101],"on":[102,194],"propose":[106],"ME2,":[107],"benchmark":[110],"consisting":[111],"1,000":[113],"math":[114],"problems":[115],"annotated":[116],"with":[117],"keypoints":[119],"corresponding":[121],"explanatory":[122],"text":[123],"references":[125],"those":[126],"elements.":[127],"Our":[128],"empirical":[129],"results":[130],"show":[131],"struggle":[135],"keypoints.":[139],"task":[142,185],"generating":[144],"keypoint-based":[145],"explanations,":[146],"open-source":[147],"also":[149],"face":[150],"notable":[151],"difficulties.":[152],"This":[153],"highlights":[154],"significant":[156],"gap":[157],"LLMs\u2019":[160],"ability":[161],"perform":[163],"grounding,":[166],"engage":[167],"visually":[169],"grounded":[170],"reasoning,":[171],"provide":[173],"contexts.":[177],"We":[178],"expect":[179],"ME2":[188],"dataset":[189],"will":[190],"catalyze":[191],"further":[192],"research":[193],"LLMs":[195],"education":[197],"promote":[199],"their":[200],"use":[201],"effective,":[203],"explanation-oriented":[204],"tutors.":[206]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}
