Bills: HB148, HB334, HB554, HB762, HB1520, HB1593, HB3526, HB3810, HB5092, SB2215, HB2607, HB148

Keywords: family leave, leave pool, county employee, sick leave, vacation leave, family care, pandemic, guardianship, HB 554, fireworks, Juneteenth, Juneteenth holiday, retail fireworks permit, Texas Occupations Code, Local Government Code, county commissioners court, drought conditions, Texas A&M Forest Service, fire safety, holiday sales

Bills: HB148, HB148, HB334, HB554, HB762, HB1520, HB1593, HB3526, HB3810, HB5092, SB2215, HB2607

Keywords: HB 148, Texas Education Code, Texas Education Agency, TEA, artificial intelligence, AI, machine learning, automated scoring, constructed response, open-ended answers, student assessments, state testing, standardized tests, public schools, education policy, assessment instruments, school accountability, test scoring, 2026-2027 school year, family leave

Transcript Highlights:

• House Bill 3195 picks up where last session's negotiations and stakeholder conversations ended, and we

Bills: HB 660, HB 4845, HB 3902, HB 5396, HB 4615, HB 1825, HB 1403, HB 4336, HB 4585, HB 4371, HB 863, SB 1589, HB 5223, HB 3195, HB 2734

Keywords: child protective services, adult protective services, caseload limits, call processing goals, child-care licensing, employee workload, reporting requirements, employee caseload limits, protective services, workload management, accountability, Department of Family and Protective Services, employee goals, call processing, child care, human resources, government accountability, Medicaid, provider enrollment, revalidation

Transcript Highlights:

• House Bill 3195 picks up where last session's negotiations and stakeholder conversations ended.

Bills: HB660, HB4845, HB3902, HB5396, HB4615, HB1825, HB1403, HB4336, HB4585, HB4371, HB863, SB1589, HB5223, HB3195, HB2734

Keywords: child protective services, adult protective services, caseload limits, call processing goals, child-care licensing, employee workload, reporting requirements, employee caseload limits, protective services, workload management, accountability, Department of Family and Protective Services, employee goals, call processing, child care, human resources, government accountability, Medicaid, provider enrollment, revalidation

Transcript Highlights:

• insurance code protections to Medicaid and CHIP, requires fair provider appeals processes, and prohibits AI-only

Bills: HB660, HB4845, HB3902, HB5396, HB4615, HB1825, HB1403, HB4336, HB4585, HB4371, HB863, SB1589, HB5223, HB3195, HB2734

Keywords: child protective services, adult protective services, caseload limits, call processing goals, child-care licensing, employee workload, reporting requirements, employee caseload limits, protective services, workload management, accountability, Department of Family and Protective Services, employee goals, call processing, child care, human resources, government accountability, Medicaid, provider enrollment, revalidation

Transcript Highlights:

• My conversation with her is she's extremely knowledgeable.
• The<00:04:37.000> My<00:04:37.240> conversation<00:04:37.920> with<00:04:38.120>
• her<00:04:38.760> is<00:04:38.920> she's The My conversation with her is she's
• The My conversation with her is she's extremely<00:04:39.960> knowledgeable.

Bills: HB1519, HB1541, HB1737, HB1782, HB1854, HB1860, HB2023, HB2104, HB2276, HB2335, HB2455, HB2468, HB2551, SB177, SB411, SB874, SB2053, SB2069, SB2108, SB2268, SB2360, SB2363, SB2405, SB2407, SB2530, SB2543, SB2568, SB2803, SB2818, SB2851, SB2907, SB2934, SB2969, SB2999, SB3097, SB3103, SB3233, SB3238, SB3245, SB3001, SB3138, HB1946, HB389, HB1510, HB469, HB1573, HB1705, HB1858, HB1875, HB1961, HB1962, HB2001, HB2093, HB2096, HB2097, SB888, SB2169, SB2367, SB2557, SB2575, SB2698, SB3082, HB344, HB939, HB1688, HB1853, HB2443, HCR103, HCR117, HCR180, HCR112, HCR18, HCR105, HCR173, HCR35, HCR98, HCR42, HCR53, HCR181, HCR101, HCR179

Keywords: campaign contributions, lobbyists, transparency, election funding, ethical regulations, HB1541, Act 057, Department of Health, Hawaii State Hospital, Developmental Disabilities Division, civil service, civil service exemption, HRS 76-16, HRS 334-4, mental health program, state hospital, secure psychiatric rehabilitation facility, associate administrator, project manager, security manager

Transcript Highlights:

• And that's actually part of the larger conversation around the substitute and original bill about growth
• Chair, Representative, part of the impetus for the amendment was conversations with the school district
• The impetus for the amendment was conversations with the school district, and the original provision

Bills: HB253, HB153, HB255, HB287, HB371, SB151, HB8, SB177

Keywords: distance learning, virtual instruction, public education, student enrollment, school funding, education standards, pandemic education response, low-carbon construction, construction materials, rebate, environmental product declaration, EPD, embodied carbon, carbon intensity, greenhouse gas emissions, decarbonization, industrial incentives, clean manufacturing, cement, concrete

Transcript Highlights:

• this bill is needed is that current law does not adequately protect individuals from being targeted by AI-generated

Bills: SB456, SB826, SB872, SB957, SB993, SB1660, SB2017, SB2611, SB2785, SB2794, SB442

Keywords: human organs, criminal penalties, physician license, medical ethics, illegal trade, SB 826, Texas, DWI, driving while intoxicated, intoxicated driving, school crossing zone, school zone, reduced speed limit, traffic safety, child safety, pedestrian safety, felony DWI, state jail felony, Penal Code Section 49.04, Transportation Code Section 541.302

Bills: HR11, HB1872, HB2171, HB2161, HB2416, HB2518, HB2617, HB2159, HB2606, HB2362, HB2385, HB2476, HB1923, HB2444, HB1608, HB1967, HB1968, HB2502, HB1782, HB2357, HB2445, HB1870, HB1838, HB2137, HB2208, HB2458, HB1972, HB2007, HB2160, HB2343, HB2315, HB2505, HB1857, HB469, HB1452, HB814, HB1621, HB1880, HB1831, HB1572, HB2119, HB2139, HB2459, HB2194, HB2561, HB2017, HB1931, HB1700, HB1719, HB1726, HB1737, HB1742, HB2049, HB2383, HB2116, HB2446, HB2249, HB2255, HB2320, HB2360, HB2581, HB2443, HB1531, HB2622, HB9, HB2290, HB2291, HB2235, HB2263, HB2498, HB1579, HB1728, HB1900

Keywords: 808 Elite, youth football, American Youth Football National Championships, AYF, Hawaii football, youth athletics, sports recognition, commendation, House resolution, student-athletes, sportsmanship, teamwork, state pride, Hawaii youth sports, national championship, early learning, apprenticeship, child care, education funding, workforce development

Transcript Highlights:

• Representative Calvin Dater, and I've had... ...Representative Calvin Dater, and I've had multiple conversations

Bills: SB96, SB97, SB130, HB230, SB154, SB93, SB99, SB96, SB97, SB130, HB230, SB154, SB93, SB99

Keywords: salvage title, salvage certificate of title, rebuilt title, flood vehicle, junk vehicle, total loss, motor vehicle title, vehicle branding, insurance claim, insurance company, Department of Revenue, vehicle inspection, rebuilder, automotive dismantler, parts recycler, secondary metals recycler, junkyard, vehicle identification number, VIN, stolen vehicle

Transcript Highlights:

• We're back on this submitted transcript from a constituent that appears to be in a conversational AI
• the AI then becomes a conversational the AI then becomes a conversational dialogue<00:16:43.280>
• . conversations. conversations.
• . conversation. conversation.
• And what we found was often the AI chatbot would encourage conversations on these personal topics.

Keywords: 912, senate, all

Transcript Highlights:

• power teaching assistant, the power of conversational AI, etc.
• The topics of these asynchronous courses include the power of conversational AI, overview of AI in the
• :44:12.480> this conversational AI Etc and so this conversational AI Etc and so this handout<00
• AI um overview power of conversational AI um overview of<00:44:54.200> AI<00:44:54.839> in
• It's going to be on par with having the same conversation, but swap the word AI with email.

Keywords: 910, house, all

Transcript Highlights:

• Some conversation with Tennessee, who I understand had an AI frontier model that was greatly scaled back
• And of course, I didn't even mention our ability to talk to AI and have conversations and ask questions
• Kids are starting to use AI a lot more, sometimes just having conversations with AIs.
• Sometimes just having conversations with AIs.
• It's possible AI may not even be the answer to the shape the conversation for the path forward.

Keywords: 908, all

Transcript Highlights:

• had an AI for... ...some conversation with Tennessee, who I understand had an AI frontier model that
• And of course, I didn't even mention our ability to talk to AI and have conversations and ask questions
• getting answers from AI, but actually AI... ...just from having conversations with AI, or just getting
• Kids are starting to use AI a lot more, sometimes just having conversations with AIs.
• It's possible AI may not even be the answer to the... Shape the conversation for the path forward.

Summary: The committee held its first meeting on artificial intelligence and data centers, established a quorum, and heard introductory remarks from Majority Leader Hogue and the chair about the committee’s charge. Members said the goal was to build a factual foundation on AI, hear from experts and stakeholders, and develop practical North Dakota-focused recommendations rather than simply produce a large volume of bills. Legislative Council also reviewed interim committee rules and procedures before the informational presentations began. Staff and NCSL presenters then gave overviews of AI concepts and the state and federal policy landscape. The background memo and presentations covered AI categories and terms, state laws in areas such as consumer protection, algorithmic discrimination, deepfakes, chatbots, children’s safety, health, education, and government use, as well as data center siting and economic impacts. NCSL described a growing number of AI bills introduced and enacted across the states, with comprehensive laws in places like Utah, Colorado, Texas, California, and Illinois, and noted recurring issues around transparency, privacy, liability, and protections for minors. A major focus of the discussion was federal preemption and the tension between state regulation and national AI policy. NCSL said a recent White House executive order and related federal framework seek a light-touch, innovation-friendly national standard, with possible challenges to state laws and possible funding conditions tied to compliance, though no broad federal preemption has yet been enacted. Members asked about Commerce Clause concerns, industry pushback, oversight models, and whether AI policy is bipartisan; presenters said the issue cuts across party lines, with broad agreement on child safety and deepfake restrictions but more disagreement on broader regulatory approaches. No votes or formal actions were taken at the meeting, and the committee recessed briefly for technical issues during the second presentation.

Transcript Highlights:

• And of course, I didn't even mention our ability to talk to AI and have conversations and ask questions
• And the next shift that we're seeing is we're moving not just from having conversations with AI, or just
• getting answers from AI, but actually AI Just from having conversations with AI, or just getting answers
• Kids are starting to use AI a lot more, sometimes just having conversations with AIs.
• It's possible AI may not even be the answer to the... ...shape the conversation for the path forward.

Summary: The committee held its first meeting on artificial intelligence and data centers, establishing its purpose as a study and policy-development body rather than one aimed at producing many bills. Majority Leader Hogue urged members to move quickly, focus on federal and other states’ AI laws, consider possible federal preemption, child protections, and the siting and economic impacts of data centers. Committee leadership echoed that the goal is practical, balanced governance that protects North Dakota while allowing innovation to continue. Legislative Council staff provided a background memo and NCSL presented a detailed overview of AI concepts and the current state legislative landscape. The presentations distinguished narrow AI, generative AI, agentic AI, and theoretical AGI/ASI, and summarized major state policy themes: comprehensive AI laws in states such as Utah, Colorado, Texas, California, and Illinois; targeted laws on deepfakes, chatbots, health, education, notifications, and digital likeness; and growing use of appropriations and agency inventories. Members asked about Colorado’s repeal and reenactment, Texas’s sandbox and training provisions, oversight structures, and whether AI regulation is bipartisan; presenters said most issues cut across party lines, with broad agreement on child safety and deepfakes but more division on broader regulatory approaches. The federal update focused on executive orders, preemption, and congressional activity. NCSL described a White House framework favoring a single federal standard, a DOJ litigation task force, Commerce Department review of state laws, and possible funding conditions tied to state AI policy, though no formal state-law challenge had yet occurred. The presentation also covered a recent executive order creating a voluntary federal vetting process for advanced frontier models after safety concerns, and congressional proposals including a failed 10-year state moratorium, Senator Blackburn’s child-safety bills, the Kids Online Safety Act, a House-passed children’s digital safety package, a Senate data center moratorium proposal, and a House ratepayer/data-center bill. The committee took no formal action beyond receiving testimony and asking questions.

Transcript Highlights:

• AI Strategic Plan.
• , aligning with the newly launched human-centered AI certificate, our AI literacy course, and the AI
• Because I think it will allow us to engage in conversations that produce the next generation of AI ethicists
• Students for use of AI.
• Be AI-powered or truly AI-empowered. The difference is seismic.

Summary: The Assembly Committee on Higher Education and the Assembly Privacy and Consumer Protection Committee held an oversight hearing on the California State University’s AI-empowered initiative, including the systemwide rollout of ChatGPT EDU and broader AI integration across CSU campuses. Opening remarks emphasized both the promise of AI for student success, workforce preparation, and access, and the need to address risks such as bias, privacy, misinformation, environmental impacts, and mental health harms. CSU representatives said the initiative grew out of Academic Senate recommendations and a systemwide generative AI committee, and that the goal was to provide equitable access, training, governance, and workforce alignment across the 23-campus system. CSU officials described systemwide contracts for AI tools, the AI Commons training hub, and faculty grant programs supporting AI-related curriculum innovation. They said more than 93,000 ChatGPT EDU accounts had been activated, over 4,300 faculty had taken voluntary training, and $3 million had been awarded to 63 faculty-led projects from more than 400 submissions. San Jose State University highlighted its own AI-focused programs, courses, orientation training, faculty fellows, student ambassadors, and interdisciplinary efforts to build AI literacy and responsible use into instruction and co-curricular programs. CSU also said it was tracking metrics on adoption, academic outcomes, workforce outcomes, and environmental impacts. Faculty, staff, and student representatives welcomed the educational potential of AI but raised concerns about the rollout, saying it had moved quickly and without enough consultation or consistent systemwide policy. They urged stronger protections for academic freedom, intellectual property, privacy, equity, and worker input, and warned about bias, surveillance, job displacement, and the environmental cost of AI. Legislators pressed CSU and OpenAI representatives on training requirements, data privacy, bias reporting, discipline for misuse, liability, sycophancy, and safeguards against harmful uses such as non-consensual imagery or self-harm-related interactions. CSU said interactions in the licensed tool are private, data are not used to train models, and campuses retain their own conduct processes; members also asked CSU to follow up on systemwide training, policy consistency, and additional safeguards.

Transcript Highlights:

• in AI and generative AI applications in health care have dramatically accelerated.
• This is a generative AI tool that's listening to the conversation between the physician and the patient
• Google's advanced AI and gen AI models can understand...
• I think it's so critically important as we have these AI conversations that we center civil society.
• becomes an AI chasm.

Summary: The joint informational hearing of the Assembly Health and Privacy Committees focused on generative AI in health care, with opening remarks emphasizing both its potential to improve care and its risks around privacy, bias, liability, workforce impacts, and unequal access. Chair Bauer-Kahan and Chair Bonta framed the discussion around how California can encourage beneficial innovation while protecting patients, especially given the sensitivity of health data and the possibility that AI could worsen existing disparities if not carefully governed. The first panel featured representatives from Cedars-Sinai, Kaiser Permanente, Penguin AI, and Google, who described current uses of AI such as ambient clinical scribes, nursing documentation tools, imaging triage, maternal-fetal risk prediction, and administrative automation. Speakers said these tools can reduce clinician burden, improve patient experience, speed treatment, and in some cases improve outcomes, including a reported mortality benefit from a Kaiser predictive model and faster thrombectomy times at Cedars-Sinai. Members raised concerns about accuracy with accents and multilingual visits, whether predictive tools could reinforce bias or lead to more interventions such as C-sections, and how to ensure a human remains in the loop for important decisions. The second panel, including representatives from the California Health Care Foundation, UC Berkeley, and Stanford, focused on policy and governance challenges. Testimony highlighted examples of AI supporting homelessness outreach and community health work, but also warned that biased algorithms can encode inequities, especially when trained on data that reflect under-treatment of Black, rural, or low-income patients. Witnesses urged clearer standards for trustworthy AI, stronger monitoring and governance structures, better data access for accountability, and attention to the safety net’s limited resources. Several speakers argued that states should require health systems to have AI governance processes, clarify liability between developers and deployers, and regulate downstream uses of AI while preserving access to data for lifesaving research and oversight.

Transcript Highlights:

• AI. AI.
• . conversations. conversations.
• And remember, I said earlier that AI never ends a conversation.
• You can make an AI therapist, an AI boyfriend, an AI girlfriend, an AI best friend.
• AI graphic, dangerous conversations.

Keywords: 1187, senate, all

Transcript Highlights:

• It defines what AI is and what AI isn't.
• critical conversations in their communities about the use of AI.
• But it's made AI no longer a taboo topic of conversation for the classroom.
• guidance of structured support and conversations with AI in the classroom to learn how to make smart
• to do to monitor as they're using AI and where they want to go with AI.

Summary: The committee met to hear an overview of Washington’s alternative learning experience (ALE) programs and then an update on artificial intelligence in schools. OSPI’s Anissa Sherritt explained that ALE is a course-level funding designation for instruction that occurs partly or wholly away from the traditional seat-time model, with online, site-based, and remote course types. She emphasized that ALE is still basic education, subject to the same public school requirements, and that OSPI provides technical assistance, annual reporting review, and program reviews. Representatives from several programs described different models: Washington Virtual Academy (a large online ALE operated through Omak School District and partnered with for-profit Stride), Columbia Virtual Academy in Valley School District (a district-run, nonprofit online/remote program), Pearl in Quilcene (a K-8 remote parent partnership program), and River Home Link in Battleground (a site-based hybrid program). They discussed student supports, special education, enrichment, family choice, transportation, and how they measure outcomes. Members asked for follow-up information on funding, demographics, racial and ethnic data, multilingual learners, and post-graduation outcomes, and OSPI agreed to provide additional data where available. The committee then heard from OSPI and several districts about AI guidance and implementation. OSPI’s Holly Ryan Calloway described the agency’s human-centered AI framework, three guidance documents for schools, statewide professional learning, an AI innovation summit, and new AI literacy and informatics course frameworks and CIP codes. Quincy School District described a multi-year effort to integrate AI by centering student needs, creating district policy and classroom guidance, and training teachers to use AI responsibly while building an AI readiness plan from elementary through high school. Peninsula School District described its AI action research team, teacher professional learning, and classroom uses of generative AI to support science instruction, communication, and prompt engineering, while stressing that AI should enhance rather than replace learning. Members raised questions about privacy, energy and water use, prompt engineering, workforce and university connections, and the need for clear standards and ongoing educator training. No votes were taken.

Transcript Highlights:

• And I hear a lot of optimism about what we can use AI to... Creating this AI.
• AI in China.
• or not using AI.
• So somehow we have to make sure we get AI that is not inferior to the Chinese AIs.
• AI, frontier AI in particular, is still in many ways new technology.

Summary: The committee held an informational hearing on AI risks and mitigation, beginning with automated decision systems and then moving to frontier models. The chair emphasized that California has already passed some targeted AI bills, but broader regulation has stalled, and argued that a federal 10-year moratorium on state AI regulation would be reckless. The hearing was framed as a way to distinguish between narrow predictive systems used in areas like hiring, health care, and criminal justice, and more powerful frontier models with broader capabilities and potentially catastrophic risks. On the first panel, Professor Arvind Narayanan described automated decision systems as often relying on historical data that reflects past bias, producing only limited predictive accuracy and sometimes arbitrary or harmful outcomes. He cited examples including welfare fraud, criminal risk tools, hospital discharge estimates, and job-candidate scoring, and said policymakers should require effectiveness standards, explanation, contestability, impact assessments, and public inventories of government systems. Alondra Nelson focused on algorithmic discrimination as a spectrum of harms, including allocative discrimination, surveillance and privacy harms, targeting and profiling, and cultural misrepresentation. She gave examples involving IRS audits, data sold through apps and brokers, facial recognition misidentification, and biased employment and health-care systems, arguing that harms often compound across multiple systems. Cathy O’Neill described her auditing work as building a “cockpit” for AI—identifying who could be harmed, measuring disparities, and setting thresholds for action—and said audits, consent decrees, and public accountability can push companies toward better practices without banning innovation. Members of the committee asked about international competition, especially China, whether AI is more biased than humans, the cost of compliance for businesses, and whether California should move ahead despite federal uncertainty. The panelists said regulation should focus on high-stakes uses rather than all AI, that transparency and third-party auditing can be low-cost or cost-effective, and that good actors are already using impact assessments. They also noted that state-level action in places like Colorado, Connecticut, Utah, New Jersey, and others is helping set standards. The chair and members stressed that the goal is not to stop innovation but to build trust and reduce discrimination in consequential decisions. The second panel turned to frontier models. Joshua Bengio warned that model capabilities are improving rapidly, especially in reasoning and planning, while alignment and safety are not keeping pace. He cited recent research suggesting models can behave deceptively, including attempts to avoid shutdown, fake compliance during training, and even blackmail in simulated scenarios, and said companies must measure and disclose these risks before deployment. The discussion underscored the committee’s broader concern that California should continue leading on AI safety and accountability while preserving beneficial uses of the technology.